SUSCEPTIBILITY GENES FOR AGE-RELATED MACULOPATHY (ARM) ON CHROMOSOME 10q26

ABSTRACT

Allelic variations in the genes PLEKHA1 and LOC387715 are identified herein as risk factor for Age Related Maculopathy (ARM). A method is therefore provided for identifying a risk of development of ARM in an individual that comprises identification of allelic variations in PLEKHA1 and/or LOC387715. Related apparatus, such as an array, are identified as being useful in implementing those methods.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of prior application Ser. No.11/448,267, filed Jun. 7, 2006, which is incorporated herein byreference in its entirety, which claims the benefit under 35 U.S.C.§119(e) to U.S. Provisional Patent Application No. 60/688,572, filedJun. 8, 2005, which is incorporated herein by reference in its entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH AND DEVELOPMENT

This invention was made with government support under Grant Nos.R01EY009859 and P30-EY008098, awarded by the National Institutes ofHealth, National Eye Institute. The government has certain rights inthis invention.

BACKGROUND

Age-related maculopathy (also known as age-related macular degeneration)is a leading cause of central blindness in the elderly population andnumerous studies support a strong underlying genetic component to thiscomplex disorder. Genome-wide linkage scans using large pedigrees,affected sib pairs, and more recently, discordant sib pairs, haveidentified a number of potential susceptibility loci (Klein et al. 1998Age-related macular degeneration. Clinical features in a large familyand linkage to chromosome 1q. Archives of Ophthalmology 116:1082-1088.;Weeks et al. 2000 A full genome scan for age-related maculopathy. HumanMolecular Genetics 9:1329-1349; Majewski et al. 2003 Age-related maculardegeneration—a genome scan in extended families. Am J Hum Genet73:540-550; Schick et al. 2003 A whole-genome screen of a quantitativetrait of age-related maculopathy in sibships from the Beaver Dam EyeStudy. Am J Hum Genet 72:1412-1424; Seddon et al. 2003 A genomewide scanfor age-related macular degeneration provides evidence for linkage toseveral chromosomal regions. Am J Hum Genet 73:780-790; Abecasis et al.2004—Age-related macular degeneration: a high-resolution genome scan forsusceptibility Loci in a population enriched for late-stage disease. AmJ Hum Genet 74:482-494; Iyengar et al. 2004 Dissection ofgenomewide-scan data in extended families reveals a major locus andoligogenic susceptibility for age-related macular degeneration. Am J HumGenet 74:20-39; Kenealy et al. 2004 Linkage analysis for age-relatedmacular degeneration supports a gene on chromosome 10q26. Mol Vis10:57-61; Schmidt et al. 2004 Ordered subset linkage analysis supports asusceptibility locus for age-related macular degeneration on chromosome16p12. BMC Genet 5:18; Weeks et al. 2004 Age-related maculopathy: agenomewide scan with continued evidence of susceptibility loci withinthe 1q31, 10q26, and 17q25 regions. Am J Hum Genet 75:174-189;Santangelo et al. 2005 A Discordant Sib-Pair Linkage Analysis ofAge-Related Macular Degeneration. Ophthalmic Genetics 26:61-68).Genome-wide linkage screen strongly implicated the 10q26 region aslikely to contain an age-related macular degeneration (AMD) gene (Weekset al. 2004); this region has also been supported by many other studiesand is the top-ranked region in a recent meta analysis (Fisher et al.2005 Meta-analysis of genome scans of age-related macular degeneration.Hum Mol Genet. 2005 Aug. 1; 14(15):2257-64). Recently, three papersappeared in Science (Edwards et al. 2005 Complement Factor HPolymorphism and Age-Related Macular Degeneration. Science 308, 421-424;Haines et al. 2005 Complement Factor H Variant Increases the Risk ofAge-Related Macular Degeneration. Science 308, 419-421. Klein et al.2005 Complement Factor H Polymorphism in Age-Related MacularDegeneration. Science 308, 385-389) identifying an allelic variant incomplement factor H (CFH) as responsible for the linkage signal seen onChromosome 1 and accounting for a significant attributable risk for ARMin both familial and sporadic cases. These findings have been confirmed(Conley et al. 2005 Candidate gene analysis suggests a role for fattyacid biosynthesis and regulation of the complement system in theetiology of age-related maculopathy. Hum Mol Genet 14: 1991-2002.;Hageman et al. (2005a) From The Cover: A common haplotype in thecomplement regulatory gene factor H (HF1/CFH) predisposes individuals toage-related macular degeneration. Proc Natl Acad Sci USA 102:7227-7232;and Zareparsi et al. 2005a Strong Association of the Y402H Variant inComplement Factor H at 1q32 with Susceptibility to Age-Related MacularDegeneration. Am J Hum Genet 77:149-53). CFH has been previouslysuspected of playing a role in ARM due to the work of Hageman andAnderson (Hageman and Mullins 1999 Molecular composition of drusen asrelated to substructural phenotype. Molecular Vision 5:28; Johnson etal. 2000 A potential role for immune complex pathogenesis in drusenformation. Experimental Eye Research 70:441-449 Complement activationand inflammatory processes in Drusen formation and age related maculardegeneration. Experimental Eye Research 73:887-896; Mullins et al. 2000Drusen associated with aging and age-related macular degenerationcontain proteins common to extracellular deposits associated withatherosclerosis, elastosis, amyloidosis, and dense deposit disease.FASEB Journal 14:835-846; Hageman et al. 2001 An integrated hypothesisthat considers drusen as biomarkers of immune-mediated processes at theRPE-Bruch's membrane interface in aging and age-related maculardegeneration. Progress in Retinal & Eye Research 20:705-732.; Johnson etal. 2001), who have shown that the subretinal deposits (drusen) that areobserved in many ARM patients contain complement factors. However untilother genes that contribute to ARM are identified, CFH remains anisolated piece of the puzzle, implicating the alternative pathway andinflammation as part of the ARM pathogenesis, but failing to account forthe unique pathology that is observed in the eye.

SUMMARY

To this end, as described below, allelic variants, including singlenucleotide polymorphisms, have been identified on Chromosome 10q26.These allelic variants are shown herein to be associated with anincreased risk of developing Age-Related Maculopathy. The allelicvariants are located within LOC387715 and/or the PLEKHA1 gene onchromosome 10q26. In one embodiment, the allelic variation is withinLOC387715.

In one non-limiting embodiment of the present invention, a method ofidentifying a human subject having an increased risk of developingAge-Related Maculopathy is provided. The method comprises identifying ina nucleic acid sample from the subject the occurrence of an allelicvariant located in Chromosome 10q26 that is associated with risk ofdeveloping Age-Related Maculopathy. In one non-limiting embodiment, theallelic variant occurs in the PLEKHA1/LOC387715/PRSS11 locus ofChromosome 10q26. For example and without limitation, the allelicvariant is an allelic variant of one or both of PLEKHA1 and LOC387715,such as, without limitation, a Ser69Ala variant in LOC387715.

In another non-limiting example, the variant is a polymorphismcorresponding to one or more of the variants identified as rs4146894,rs10490924, rs1045216, rs1882907, rs760336, rs763720, rs800292,rs1483883 and rs1853886. The allelic variant may be, without limitation,a mutation that produces one of a non-functional gene product andaltered expression of a gene product, such as one or more of aframeshift mutation, a promoter mutation and a splicing mutation.

In one embodiment, the method comprises further identifying in a nucleicacid sample from the subject the occurrence of an allelic variant ofcomplement factor H, such as, without limitation, a variantcorresponding to the single nucleotide polymorphism identified asrs1853883.

The method may employ any useful technology, such as without limitation:a nucleic acid amplification assay, such as one of a PCR, a reversetranscriptase PCR (RT-PCR), an isothermic amplification, a nucleic acidsequence based amplification (NASBA), a 5′ fluorescence nuclease assay(for example TAQMAN assay), a molecular beacon assay and a rollingcircle amplification. The allelic variation may be identified using anarray that typically comprises one or more reagents for identifying in anucleic acid sample from the subject the occurrence of an allelicvariation corresponding to two or more of the single nucleotidepolymorphisms identified as rs4146894, rs1045216, rs10490924, rs1882907,rs760336, rs763720, rs800292, rs1483883 and rs1853886.

In another non-limiting embodiment, an array is provided comprising oneor more reagents sequences for identifying in a nucleic acid sample froma subject the occurrence of an allelic variation located on Chromosome10q26 that is associated with risk of development of Age-RelatedMaculopathy. The allelic variation may, without limitation, may occur inthe PLEKHA1/LOC387715/PRSS11 locus of Chromosome 10q26, and, forexample, may correspond to one or more single nucleotide polymorphismsidentified as rs4146894, rs1045216, rs10490924, rs1882907, rs760336,rs763720, rs800292, rs1483883 and rs1853886.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1: Location of CIDR and locally genotyped SNPs with respect tocandidate genes. Positions, distances and nucleotide position alongChromosome 10 are derived from NCBI Entrez-gene and SNP databases. FIG.1-2 is split at line A-A′.

FIG. 2: Single-point and multipoint linkage results on Chromosome 10.FIG. 2-1 summarizes the results when all SNPs were used. FIG. 2-2summarizes the results when only H-clust SNPs were used for analysis.The peaks marked with “F”, represent likely false peaks due to highSNP-SNP LD, while the peaks marked “G” and “P,” correspond to the locicontaining GRK5 and PLEKHA1 respectively. The horizontal lines indicatethe 1-unit support interval of multipoint S_(all) (maximum S_(all)−1).

FIGS. 3A-3D: Linkage disequilibrium patterns on chromosome 10 based on196 CIDR SNPs and 179 unrelated controls. FIG. 3A: The false peak at 135cM (see FIG. 2), FIG. 3B: The false peak at 142 cM (see FIG. 2), FIG.3C: Linkage peak. FIGS. 3D-1, 3D-2, 3D-3 and 3D-4 are larger versions ofFIG. 3C, divided at lines B-B′, C-C′ and D-D′. The SNP with the largestS_(all) in false peaks (FIGS. 3A and 3B) are shown in gray andsignificant SNPs overlying the 5 genes(GRK5/RGS10/PLEKHA1/LOC387715/PRSS11) from CCREL (Table 5) are shown ingray in the true linkage peak. Shades of gray indicate significant LDbetween SNP pairs (dark gray squares with no numbers indicate pairwiseD′=1), white squares indicate no evidence of significant LD and graysquares with no numbers indicate pairwise D′ of 1 without statisticalsignificance. LD is measured using D′ and the numbers in the squaresgive pair wise LD in D′*100.

FIG. 4: Multipoint linkage results on Chromosome 1. FIG. 4-1 summarizesresults when all SNPs were used and FIG. 4-2 summarizes results whenonly H-clust SNPs were used for analysis. The peaks marked with “F”represent likely false peaks due to high SNP-SNP LD, while the peaksmarked with “C” correspond to the CFH gene. The horizontal linesindicate the 1-unit support interval of multipoint S_(all) (maximumS_(all) in CFH-1).

FIGS. 5A-5C provide: linkage disequilibrium patterns on chromosome 1based on 679 CIDR SNPs and 179 unrelated controls. FIG. 5A: The falsepeak at 188 cM (see FIG. 4), FIG. 5B: The false peak at 202 cM (see FIG.4), FIG. 5C: The linkage peak overlying the CFH loci. The SNP with thelargest S_(all) in false peaks are shown in gray and significant SNPsoverlying CFH from CCREL (Table 5) are shown in grey in the true linkagepeak. Shades of gray indicate significant LD between SNP pairs (darkgray squares with no numbers indicate pairwise D′=1), white squaresindicate no evidence of significant LD and gray squares with no numbersindicate pairwise D′ of 1 without statistical significance. LD ismeasured using D′ and the numbers in the squares give pair wise LD inD′*100.

FIGS. 6A and 6B: FIG. 6A: Linkage disequilibrium patterns in the GRK5(Block 1), RGS10 (SNP 6), PLEKHA1 (Block 2), LOC387715 (Block 3), PRSS11(Block 4). FIG. 6B: Linkage disequilibrium patterns in CFH (Block 1).Shades of gray indicate significant LD between SNP pairs (dark graysquares with no numbers indicate pairwise D′=1), white squares indicateno evidence of significant LD and gray squares with no numbers indicatepairwise D′ of 1 without statistical significance. LD is measured usingD′ and the numbers in the squares give pair wise LD in D′*100.Significant SNPs from the CCREL allele test are highlighted in gray (seeTable 6). Three SNPs (rs6428352, rs12258692 and rs11538141) were notincluded because of very low heterozygosity and one SNP, rs2736911, wasnot included because it was uninformative. Note the blocks were drawn toclearly show the position of the genes and do not represent haplotypeblocks.

FIG. 7 shows estimated crude ORs and 95% CIs for CFH, ELOVL4, PLKEHA1,and LOC387715 genes. Carriers of one or two risk alleles (RR+RN) arecompared to those subjects homozygote for the non-risk allele (NN). Thesolid lines denote the 95% CI corresponding to an OR (open circle). Thedotted vertical line marks the null value of an OR of 1. The contraststhat were evaluated in AREDS and CHS cohorts are given on the verticalaxis.

FIG. 8 shows estimated ORs and 95% CIs for CFH. A: OR_(dom) forevaluation of dominance effects (CT+CC vs. TT). B: OR_(het) forevaluation of the risk of heterozygotes (CT vs. TT). C: OR_(rec) forevaluation of recessive effects (CC vs. CT+TT). D: OR_(hom) forevaluation of the risk of homozygotes (CC vs. TT). The dotted verticalline marks the null value of OR of 1.

FIG. 9 shows estimated ORs and 95% CIs for ELOVL4. A: OR_(dom) forevaluation of dominance effects (AG+GG vs. AA). B: OR_(het) forevaluation of the risk of heterozygotes (AG vs. AA). C: OR_(rec) forevaluation of recessive effects (GG vs. AG+AA). D: OR_(hom) forevaluation of the risk of homozygotes (GG vs. AA). The dotted verticalline marks the null value of OR of 1.

FIG. 10 shows estimated ORs and 95% CIs for LOC387715. A: OR_(dom) forevaluation of dominance effects (GT+TT vs. GG). B: OR_(het) forevaluation of the risk of heterozygotes (GT vs. GG). C: OR_(rec) forevaluation of recessive effects (TT vs. GT+GG). D: OR_(hom) forevaluation of the risk of homozygotes (TT vs. GG). The dotted verticalline marks the null value of OR of 1.

FIG. 11 shows estimated ORs and 95% CIs for PLEKHA1. A: OR_(dom) forevaluation of dominance effects (AG+AA vs. GG). B: OR_(het) forevaluation of the risk of heterozygotes (AG vs. GG). C: OR_(rec) forevaluation of recessive effects (AA vs. AG+GG). D: OR_(hom) forevaluation of the risk of homozygotes (AA vs. GG). The dotted verticalline marks the null value of OR of 1.

FIG. 12 provides estimated ORs and 95% CIs, derived from data setsincluded in meta-analysis of Y402H in CFH, and pooled estimates fromfixed and random effect models. The top figure shows OR_(het) (OR for CTheterozygotes compared to TT) and the bottom figure shows OR_(hom) (ORfor CC homozygotes compared to TT). ‘Hage-C’ and ‘Hage-I’ denoteestimates derived from the Columbia and Iowa cohorts of Hageman et al.(Hageman, G. S., et al. (2005) A common haplotype in the complementregulatory gene factor H (HF1/CFH) predisposes individuals toage-related macular degeneration. Proc Natl Acad Sci USA. 2005 May 17;102(20):7227-32. Epub 2005 May 3), respectively, and ‘Jakobs’ denotesestimates from the Jakobsdottir et al. paper (Jakobsdottir, J., et al.(2005) Susceptibility genes for age-related maculopathy on chromosome10q26. Am J Hum Genet. 77, 389-407). “Fixed” denotes pooled estimatesderived from all the studies assuming the between study variability isdue to chance. ‘Random’ denotes pooled estimates derived from all thestudies allowing for heterogeneity across studies. ‘n_(AMD)’ is thetotal number of ARM cases included in the estimates and ‘n_(con)’ is thetotal number of controls without ARM included in the estimates. Thedotted vertical line marks the point estimate of the pooled OR underhomogeneity (‘Fixed’).

FIG. 13 provides: A: Genotype frequencies (%) in unrelated ARM cases,across cohorts included in meta-analysis of Y402H in CFH. B: Genotypefrequencies (%) in unrelated controls without ARM, across studiesincluded in meta-analysis of Y402H in CFH. “Hage-C” and “Hage-I” denoteestimates derived from the Columbia and Iowa cohorts of Hageman et al.,respectively, and “Jakobs” denotes estimates from the Jakobsdottir etal. paper.

FIG. 14 provides estimated ORs and 95% CIs, derived from data setsincluded in meta-analysis of S69A in LOC387715, and pooled estimatesfrom fixed and random effect models. The top figure shows OR_(het) (ORfor GT heterozygotes compared to GG) and the bottom figure showsOR_(hom) (OR for TT homozygotes compared to GG). “Jakobs” denotesestimates from the Jakobsdottir et al. paper (Jakobsdottir, J., et al.(2005) Susceptibility genes for age-related maculopathy on chromosome10q26. Am J Hum Genet. 77, 389-407). “Fixed” denotes pooled estimatesderived from all the studies assuming the between study variability isdue to chance. “Random” denotes pooled estimates derived from all thestudies allowing for heterogeneity across studies. “n_(ARM)” is thetotal number of ARM cases included in the estimates and “n_(con)” is thetotal number of controls without ARM included in the estimates. Thedotted vertical line marks the point estimate of the pooled OR underhomogeneity (‘Fixed’)

FIG. 15 provides: A: Genotype frequencies (%) in unrelated ARM cases,across cohorts included in meta-analysis of S69A in LOC387715. B:Genotype frequencies (%) in unrelated controls without ARM, acrossstudies included in meta-analysis of S69A in LOC387715. “Jakobs” denotesestimates from the Jakobsdottir et al. paper.

FIG. 16 provides an amino acid (SEQ ID NO: 19) and nucleotide sequence(SEQ ID NO: 20) for LOC387715.

DETAILED DESCRIPTION

As described below, allelic variants, including single nucleotidepolymorphisms, have been identified on Chromosome 10826. These allelicvariants are shown herein to be associated with an increased risk ofdeveloping Age-Related Maculopathy. Example 1 identified PLEKHA1 and/orLOC387715 as loci of allelic variants related to ARM. Further studies,as shown in Example 2, confirm and add further support to the identifiedrelevance of variations in LOC387715 as a marker for ARM. The relevanceof variations of PLEKHA1 as a marker for ARM has not been ruled out butthe evidence from the most recent genetic studies more stronglyimplicate the variants within the LOC387715 gene.

Methods are therefore provided for identifying a human subject having anincreased risk of developing Age-Related Maculopathy (ARM). The methodscomprise identifying in a nucleic acid sample from the subject theoccurrence of an allelic variant or a specific haplotype (comprised ofseveral allelic variants) of PLEKHA1 and/or LOC387715, and in onenon-limiting embodiment, LOC387715. Specific single nucleotidepolymorphisms (SNPs) have been identified within these loci, including,without limitation, those SNPs identified as rs4146894, rs1045216,rs10490924, rs1882907, rs760336 and rs763720. The method may furthercomprise identifying an allelic variation in Complement Factor H (CFH),such as, without limitation the allelic variant identified as rs1853883.

As used herein, an “allelic variation” refers to a variation in thenucleic acid and typically primary amino acid sequence of a gene in oneor more alleles in a subject, such as a human patient. Allelicvariations include single or multiple nucleic acid and amino acidsubstitutions, additions or deletions that have any one of a number ofeffect on protein expression, including without limitation: promoteractivity that regulates transcription, frameshift, early proteintermination, protein mis-folding, altered protein processing,destruction (or enhancement) of active sites or binding sites of aprotein, mis-splicing of an mRNA or any other property of a nucleic acidor protein that effects the expression and/or function of the final geneproducts. An amino acid and nucleic acid sequence variation may or maynot have be silent, that is, no phenotypic effect, such as risk ofdisease, can be associated with that sequence variation. On the otherhad, an allelic variation is a variation in a consensus “wild type”nucleic acid or amino acid sequence to which risk of a disease state,such as ARM, can be attributed, associated or otherwise connected, forexample and without limitation, by statistical methods is describedherein. Thus, the LOC387715 single nucleotide polymorphism identified asrs10490924 (Ser69A1a) is an allelic variation.

A large number of methods, including high throughput methods, areavailable for detection of SNPs and/or other allelic variations, forexample and without limitation the PCR and Restriction Fragment LengthPolymorphisms methods described in the Examples below. In oneembodiment, DNA from a sample is sequenced (resequenced) by any methodto identify a SNP or small alleic variation. A large variety ofresequencing methods are known in the art, including high-throughputmethods. Amplification-based methods also are available to identifyallelic variations, such as SNPs, including, without limitation: PCR,reverse transcriptase PCR (RT-PCR), isothermic amplification, nucleicacid sequence based amplification (NASBA), 5′ fluorescence nucleaseassay (for example TAQMAN assay), molecular beacon assay and rollingcircle amplification. Other methods, such as Restriction Fragment LengthPolymorphisms RFLP, also maybe employed—as is appropriate and effectiveto identify variant allele(s). Assays may be multiplexed, meaning two ormore reactions are carried out simultaneously in the same physicallocation, such as in the same tube or position on an array—so long asthe reaction products of the multiplexed reactions can be distinguished.As a non-limiting example, TAQMAN or molecular beacon assays can bemultiplexed by use of and by monitoring of accumulation or depletion oftwo different fluorochromes corresponding to two differentsequence-specific probes. In most cases, the appropriate method isdictated by personal choice and experience, equipment and reagents onhand, the need for high throughput and/or multiplexed methods, cost,accuracy of the method, and the skill level, of technicians running theassay. Design and implementation of those techniques are broadly-knownand are well within the abilities of those of average skill in the art.

In the implementation of the methods provided herein, an array may beutilized. Arrays are particularly useful in implementing high-throughputassays. The array typically comprises one or more reagents, for exampleand without limitation, nucleic acid primers and/or probes, foridentifying in a nucleic acid sample from a human subject the occurrenceof an allelic variation corresponding to one or more single nucleotidepolymorphisms identified in LOC387715 and/or PLEKHA1, such as, withoutlimitation, the SNPs identified as: rs4146894, rs1045216, rs10490924,rs1882907, rs760336, rs763720, rs800292, rs1483883, rs1853883 andrs1853886. An array would allow simultaneous testing and identificationof one or more allelic variation in LOC387715and/or PLEKHA1, such as,without limitation, the SNPs identified as: rs4146894, rs1045216,rs10490924, rs1882907, rs760336, rs763720, rs800292, rs1483883,rs1853883 and rs1853886 as well as simultaneous identification ofallelic variations in CFH, other genes/loci and controlgenes/loci/nucleic acids.

As used herein, the term “array” refers to reagents for facilitatingidentification of allelic variations in a gene located at two or moreidentifiable locations. In one embodiment, an array is an apparatushaving two or more discrete, identifiable reaction chambers, such as,without limitation a 96-well dish, in which reactions comprisingidentified constituents are performed. In an exemplary embodiment, twoor more nucleic acid primers or probes are immobilized onto a substratein a spatially addressable manner so that each individual primer orprobe is located at a different and (addressable) identifiable locationon the substrate. Substrates include, without limitation, multi-wellplates, silicon chips and beads. In one embodiment, the array comprisestwo or more sets of beads, with each bead set having an identifiablemarker, such as a quantum dot or fluorescent tag, so that the beads areindividually identifiable using, for example and without limitation, aflow cytometer. In one embodiment, in the context of the presentdisclosure, an array may be a multi-well plate containing two or morewell reaction chambers with primers for amplifying DNA to identify SNPsor probes for binding specific sequences. As such, reagents, such asprobes and primers may be bound or otherwise deposited onto or intospecific locations on an array. Regeants may be in any suitable form,including, without limitation: in solution, dried, lyophilized orglassified.

Useful Array technologies include, for example and without limitation anAffymetrix GeneChip® Array, for example, GeneChip® CustomSeq®Resequencing Arrays (commercially available from Affymetrix Inc. ofSanta Clara, Calif.) and like technologies. Informatics and/orstatistical software or other computer-implemented processes foranalyzing array data and/or identifying genetic risk factors from dataobtained from a patient sample, are known in the art.

As used herein, a “reagent for identifying in a nucleic acid sample froma subject the occurrence of an allelic variation” that either isidentified specifically or is identified in the context of a gene orlocus, refers to a reagent that enables identification of that specificallelic variation by any suitable method, for example and withoutlimitations, by PCR, resequencing 5′ exonuclease (TaqMan) assay and/orarray or high-throughput assays. Non-limiting examples of such reagentsinclude sequence-specific primers, primer sets and probes for use in anyuseful assay system. Primers and probes may take any useful form, buttypically are nucleic acids, but may be nucleic acid analogs, such as,without limitation phosphorothiates.

In Example 1, a family-based linkage study and a case-controlassociation study were undertaken using a high density SNP panel in tworegions of linkage on 1q31 and 10q26. SNP linkage and associationresults for Chromosome 1q31 confirmed that the peak of linkage and thestrongest associations with ARM were localized over the CFH gene. Bothfamily and case-control data on Chromosome 10q26 were analyzed toidentify the next major ARM susceptibility-related gene.

A follow-up study, described in Example 2, utilized a nestedcase-control design with subjects originally recruited through aCardiovascular Health Study (CHS), a population-based cohort for whichAge-Related Maculopathy (ARM) status was not a factor for ascertainment,and an Age-Related Eye Disease Study (AREDS), a population-based cohortfor which ARM status was a factor for ascertainment. These cohorts wereutilized to investigate the CFH, PLEKHA1, LOC387715 and ELOVL4 genes inARM susceptibility in two cohorts with different ascertainment schemes.Furthermore, these two cohorts plus eleven and four additionalcase-control studies were included in a meta-analysis for CFH andLOC387715 respectively. CFH was significantly associated with ARM statusin both cohorts (p<0.00001) and meta-analysis confirmed that the riskallele in the heterozygous or homozygous state (OR, 2.4 and 6.2; 95% CI(Confidence Interval), 2.2-2.7 and 5.4-7.2 respectively) confers thissusceptibility. LOC387715 was significantly associated with ARM statusin both cohorts (p<0.00001) and meta-analysis confirmed that the riskallele in the heterozygous and homozygous state (OR, 2.5 and 7.3; 95%CI, 2.2-2.9 and 5.7-9.4 respectively) confers this susceptibility.PLEKHA1, which is closely linked to LOC387715, was significantlyassociated with ARM status in the AREDS cohort but not the CHS cohortand ELOVL4 was not significantly associated ARM in either cohort. Thisstudy provides additional support for the CFH and LOC387715 genes in ARMsusceptibility via the evaluation of cohorts that had differentascertainment schemes in regard to ARM status as well as further supportthrough a meta-analysis.

Example 1 Material and Methods Families and Case-Control Cohort

A total of 612 AMD families and 184 unrelated controls were sent to theCenter for Inherited Disease Research (CIDR) for genotyping. Due topossible population substructure, analysis was restricted to theCaucasian subset of our data. The Caucasian subset had 594 AMD families,containing 1443 genotyped individuals, and 179 unrelated controls. TheCaucasian families contained 430 genotyped affected sib pairs, 38genotyped affected avuncular pairs, and 52 genotyped affected firstcousin pairs.

A total of 323 Caucasian families, 117 unrelated controls, and 196unrelated cases were also genotyped locally for additional SNPs. Thelocal subset contained 824 genotyped individuals, 298 genotyped affectedsib pairs, 23 genotyped affected avuncular pairs and 38 genotypedaffected first cousin pairs. PedStats from the Merlin package (Abecasiset al. 2000) was used to easily get summary counts on family data.

Affection Status Models

Three classification models (A, B and C) were defined for the severityof ARM status (Weeks et al. 2004). For simplicity, attention wasrestricted to “Type A” affecteds, the most stringent and conservativediagnosis. Only unrelated controls were unaffected under all threediagnostic models were used. Unaffected individuals were those for whomeye care records and/or fundus photographs indicated either no evidenceof any macular changes (including drusen) or a small number (less than10) of hard drusen (50 microns or less in diameter) without any otherRPE changes. Individuals with large numbers of extramacular drusen werenot coded as unaffected when this information was available.

In efforts to examine specific ARM sub-phenotypes, only those with endstage disease, either those with evidence of a choroidal neovascularmembrane (CNV) in either eye, or those with geographic atrophic (GA) ineither eye were chosen to look at. There are a significant number ofindividuals who have been reported to have both geographic atrophy andCNV, though this is problematic since it is often difficult to tell inthese cases if the geographic atrophy is secondary to the damage fromthe CNV or from the treatment given to limit the CNV growth (i.e.,laser, surgery, or photodynamic therapy). Because it is often difficultto discern from photographs or records if a person had GA in an eyeprior to the development of a CNV, those patients who had bothpathologies within the CNV group were included. However, only a subsetof this overlapping group were allowed to be included within thegeographic atrophy group, specifically if there was reported geographicatrophy in one eye that did not have evidence of a CNV. Table 1 showsthe numbers of such individuals for each of the three case sets. Thisapproach may have excluded a small proportion of individuals from thegeographic atrophy group who had asymmetric geographic atrophy prior tothe development of CNV in the same eye or who may have had bilateralgeographic atrophy, but developed CNV's in both eyes.

TABLE 1 The distribution of subphenotypes in patients with advanced ARM.Numbers in parentheses refer to number of individuals with both CNV andGA and also included in GA group (see the text for the selectioncriteria) for odds ratio and attributable risk estimation andassociation tests. Cases from Cases from Local unrelated CIDR familieslocal families cases GA no GA GA no GA GA no GA CNV 220 (76) 187 130(45) 106 71 (17) 59 no CNV 108 62 57 28 40 26

Pedigree and Genotyping Errors and Data Handling

The program PedCheck (O'Connell and Weeks 1998 PedCheck: A program foridentifying genotype incompatibilities in linkage analysis. Am J HumGenet 63:259-266) was used to check for Mendelian inconsistencies. Sinceit can be extremely difficult to determine which genotype within a smallfamily is erroneous (Mukhopadhyay et al. 2004 Comparative study ofmultipoint methods for genotype error detection. Hum-Hered 58:175-189),all genotypes were set at each problematic marker to missing within eachfamily containing a Mendelian inconsistency. Mega2 (Mukhopadhyay et al.,Mega2: data-handling for facilitating genetic linkage and associationanalyses. Bioinformatics 2005 May 15; 21(10):2556-7) was used to set upfiles for linkage analysis as well as for allele frequency estimation bygene-counting.

Allele Frequencies and Hardy Weinberg Equilibrium

The allele frequencies used in the linkage analyses were estimated, bydirect counting, from the unrelated and unaffected controls. Allcontrols were unaffected under all three affection status models.Genotyped spouses that had no children or children who are not yet partof the study were combined with the controls for this study. The exacttest of Hardy-Weinberg equilibrium, implemented in Mega2 (Mukhopadhyayet al. 2005 Mega2: data-handling for facilitating genetic linkage andassociation analyses. Bioinformatics), was performed on our SNPs.

Mendel version 5 (Lange et al. 2001 MENDEL Version 4.0: A completepackage for the exact genetic analysis of discrete traits in pedigreeand population data sets. Am J of Hum Genet 69 (Supplement):A1886) wasalso used to estimate allele frequencies directly from the family data,as Mendel properly accounts for relatedness of the subjects whileestimating the allele frequencies. Since the majority of the genotypedfamily members are affected, these estimates are quite close toestimates obtained using unrelated affected cases.

Genetic Map

The Rutgers combined linkage-physical map (version 2.0) (Kong et al.2004 A combined linkage-physical map of the human genome. Am J Hum Genet75:1143-1148) was used to predict the genetic position of the SNPs thatwere not already present in the Rutgers map. Since the distribution ofour SNPs is very dense in the regions of interest, the estimatedrecombination between several SNPs was zero; in these cases therecombination was set to 0.000001. The physical positions were obtainedfor all of our SNPs from NCBI dbSNP database (human build 35).

Linkage Disequilibrium Structure

Ignoring high linkage disequilibrium (LD) between SNPs when performinglinkage analysis can result in false positive findings (Schaid et al.2002 Caution on pedigree haplotype inference with software that assumeslinkage equilibrium. Am J Hum Genet 71:992-995; Huang et al. 2004Ignoring linkage disequilibrium among tightly linked markers inducesfalse-positive evidence of linkage for affected sib pair analysis. Am JHum Genet 75:1106-1112). Efforts to take high SNP-SNP LD into accountincluded the following:

1. The LD structure in unrelated controls was studied using theH-cluster method (Rinaldo et al. 2005 Characterization of multilocuslinkage disequilibrium. Genetic Epidemiology 28:193-206), which isimplemented in R (R Development Core Team 2004 R: A language andenvironment for statistical computing. R Foundation for StatisticalComputing, Vienna, Austria. ISBN 3-900051-07-0). The aim was todetermine haplotype-tagging SNPs (htSNPs) for linkage analysis. Themethod uses hierarchical clustering to cluster highly correlated SNPs.After the clustering, the H-clust method chooses a htSNP for eachcluster; the htSNP is the SNP that is most correlated with all otherSNPs in the cluster. The SNPs were chosen so that each SNP had acorrelation coefficient (r²) greater than 0.5 with at least one htSNP;

2. The program HaploView (Barrett et al. 2005 Haploview: analysis andvisualization of LD and haplotype maps. Bioinformatics 21:263-265.) wasused to get a graphical view of SNP-SNP LD along Chromosomes 1 and 10;and

3. Haplotype-based association analyses was performed using two andthree-SNP moving windows (See below).

Linkage Analysis

1. Single point analysis. As in our previous study (Weeks et al. 2004),LOD scores were computed under single simple dominant model (diseaseallele frequency=0.0001 and penetrance vector=[0.01 0.90 0.90]). Due tothe complexities and late-onset of the ARM phenotype, only two diseasephenotypes were used: “affected under model A” and “unknown”. ParametricLOD scores were computed under heterogeneity (HLOD), while model-freeLOD scores were computed using the linear S_(all) statistic. Both scoreswere computed using Allegro (Gudbjartsson et al. 2000 Allegro, a newcomputer program for multipoint linkage analysis. Nat Genet 25:12-13.).

2. Multipoint analysis ignoring linkage disequilibrium. Sinceinter-marker distances are often very small, LD between SNPs can be highand thus violate the assumption of no LD made by most linkage analysisprograms. Multipoint analyses ignoring LD were performed using Allegro(Gudbjartsson et al. 2000). Both HLODs and S_(all) statistics werecomputed.

3. Multipoint analysis using htSNPs. When using only the htSNPs for LODscore calculation, the number of SNPs decreases to 533 on Chromosome 1and 159 on Chromosome 10. Multipoint LOD analyses were done as before(Weeks et al. 2004). The SNPs that were omitted fit well to the SNP-SNPLD structure estimated with HaploView (Barrett et al. 2005).

Association Analysis

In order to use all of the cases from the families, the new CCRELprogram (Browning et al. 2005 Case-Control single-marker and haplotypicassociation analysis of pedigree data. Genet Epidomiol 28:110-122.) wasused, which permits one to test for association using related casessimultaneously with unrelated controls. CCREL was used to analyze SNPsunder the linkage peak on Chromosomes 1 and 10 to test for association.The CCREL test accounts for biologically-related subjects by calculatingeffective number of cases and controls. For these analyses, unrelatedcontrols were given a “normal” phenotype, while family members that arenot affected with ‘Type A’ ARM were given an “unknown” phenotype (TheCCREL approach has not yet been extended to permit one to simultaneouslyuse both related cases and related controls). The effective number ofcontrols for each SNP used for association testing is therefore numberof controls genotyped for that SNP. An allelic test, a haplotype testusing two SNP sliding window, a haplotype test using three SNP slidingwindow and a genotype test were performed. The CCREL R package was usedfor analysis as provided by the authors (Browning et al. 2005).

GIST Analysis

To explore which allele/SNP contributes the most to the linkage signal,the genotype-IBD sharing test (GIST) was performed using locallygenotyped SNPs and significant SNPs, from the CCREL test, around thelinkage peak on both Chromosomes 1 and 10. The GIST test determines ifan allele or an allele in LD with it accounts in part for observedlinkage signal (Li et al. 2004). Weights were computed for each affectedsibship under three different disease models (recessive, dominant,additive)—these weights are unbiased under the null hypothesis of nodisease-marker association. The correlation between the family weightvariable and nonparametric linkage (NPL) score is the basis of the teststatistic. Since the GIST test is currently only applicable to affectedsib pair families, families were broken into their component nuclearfamilies before computing the NPL scores. Since the underlying diseasemodel was unknown, we tested under three different disease models(recessive, dominant, additive), and then took the maximum, using ap-value that was adjusted for multiple testing over the three models.

Tripartite Analyses

Analyses were carried out in three sequential steps. First, the set ofdata that had been genotyped at CIDR was analyzed. Second, after locallygenotyping 8 additional SNPs in the PLEKHA1/LOC387715/PRSS11 region onChromosome 10, the locally-genotyped data set was then analyzed. Notethat all of the known non-synonymous SNPs in the PLEKHA1 through PRSS11region were investigated. As these two data sets differ in size andcomposition, it is most straightforward to analyze them separately(Table 2). Allele frequency estimation, CCREL association testing, andGIST testing were carried out on both of these (overlapping) data setsas described above. Third, we tested for interaction between theChromosome 1 and Chromosome 10 regions was tested, as well as examinedwhether or not the risk differed as a function of the presence of eithergeographic atrophy or choroidal neovascular membranes.

TABLE 2 Summary of statistical analysis and sample sizes in each part.PART Analysis Set of SNPs and sample used Results I htSNP CIDR SNPs on179 controls selection SNP-SNP LD CIDR SNPs on 179 controls FIGS. 3A-3Dand 5A-5C Linkage CIDR SNPs and htSNPs on FIGS. 594 ARM families 2, 3Allele Mendel 5 on 594 ARM Table 5 frequencies families Counting on 179controls CCREL CIDR SNPs on 594 ARM Table 5 families and 179 controlsGIST 594 ARM families broken Table 5 down to 734 typed nuclear familiesII Allele All SNPs (CIDR and local) Table 6 frequencies Mendel 5 on 323ARM families Counting 117 controls CCREL CIDR SNPs and local SNPs Table6 within genes, 323 families and 117 controls GIST 323 ARM familiesbroken Table 6 down to 407 typed nuclear families SNP-SNP LD CIDR andlocal SNPs within FIG. 6 genes on 117 unrelated controls III InteractionSee GIST in I and II above Tables with GIST 5, 6 Logistic CIDR SNPs, 577cases and Table 7 regression 179 controls OR and AR CIDR SNPs, 577 casesand Table 8 179 controls Local SNPs, 517 cases (321 from families, 196sporadic) and 117 controls OR and AR Table 9 of subtypes CIDR SNPs CNV:407 cases and 179 controls GA: 184 cases and 179 controls Local SNPsCNV: 366 cases and 117 controls GA: 159 and 117 controls

Part I: Analysis of CIDR SNPs CIDR SNP Genotyping

To identify the responsible gene on Chromosome 10q26, the Center forInherited Disease Research (CIDR) carried out high-density custom SNPgenotyping of 612 AMD families and 184 unrelated controls with 199 SNPsspanning 13.4 Mbp (26.7 cM) spanning our region of interest. Foranalysis 196 SNPs were used: three were skipped due to lack ofpolymorphism in the controls (when this was checked within the familydata, the missing allele was extremely rare and only present inheterozygotes). 684 SNPs spanning 45.7 Mbp (47.1 cM) on Chromosome 1q31were also genotyped; five SNPs were skipped due to lack of polymorphismin the controls—the missing allele was either not present or very rareand only present in heterozygotes in the family data. See Table 3 forthe correspondence between allele labels provided herein and the actualalleles, and, for non-synonymous SNPs, the amino acid change.

TABLE 3 Allele labeling. For each marker investigated, allele labels,amino acid change of non- synonymous SNPs, allele frequency in CIDRcontrols (179) and allele frequency in local controls (117 overlap CIDRcontrols) and HWE p-value of the exact test. SNP Allele Label Amino AcidCIDR controls Local controls HWE P-value rs6658788 A 1 0.511 0.483 0.58G 2 0.489 0.517 rs1538687 A 1 0.693 0.658 0.41 G 2 0.307 0.342 rs1416962T 1 0.648 0.607 0.44 C 2 0.352 0.393 rs946755 T 1 0.656 0.620 0.70 C 20.344 0.380 rs6428352 T 1 0.997 0.996 1.00 C 2 0.003 0.004 rs800292 A 1= Ile Ile62Val 0.232 0.269 0.82 G 2 = Val 0.768 0.731 rs1061170 T 1 =Tyr Tyr402His 0.690 0.26 C 2 = His 0.310 rs10922093 G 1 0.295 0.66 A 20.705 rs70620 T 1 0.173 0.150 0.28 C 2 0.827 0.850 rs1853883 G 1 0.5110.568 0.45 C 2 0.489 0.432 rs1360558 A 1 0.397 0.389 0.70 G 2 0.6030.611 rs955927 T 1 0.609 0.615 0.85 A 2 0.391 0.385 rs4350226 A 1 0.9050.897 0.34 G 2 0.095 0.103 rs4752266 A 1 0.777 0.774 0.18 G 2 0.2230.226 rs915394 T 1 0.813 0.791 1.00 A 2 0.187 0.209 rs1268947 G 1 0.8830.885 0.65 C 2 0.117 0.115 rs1537576 G 1 0.567 0.581 0.35 C 2 0.4330.419 rs2039488 T 1 0.885 0.885 0.01 C 2 0.115 0.115 rs1467813 T 1 0.2930.295 0.66 C 2 0.707 0.705 rs927427 A 1 0.464 0.487 0.10 G 2 0.536 0.513rs4146894 A 1 0.466 0.474 1.00 G 2 0.534 0.526 rs12258692 C 1 = ProPro233Arg 1.000 — G 2 = Arg 0.000 rs4405249 T 1 0.158 1.00 C 2 0.842rs1045216 G 1 = Ala Ala320Thr 0.573 0.46 A 2 = Thr 0.427 rs1882907 A 10.813 0.816 0.76 G 2 0.187 0.184 rs10490923 G 1 = Arg His3Arg 0.859 0.39A 2 = His 0.141 rs2736911 C 1 = Arg Arg38Ter 0.881 1.00 T 2 = Ter 0.119rs10490924 G 1 = Ala Ser69Ala 0.807 0.21 T 2 = Ser 0.193 rs11538141 A 1= Glu Gly54Glu 0.995 1.00 G 2 = Gly 0.005 rs760336 T 1 0.520 0.526 0.58C 2 0.480 0.474 rs763720 A 1 0.212 0.226 0.79 G 2 0.788 0.774 rs1803403T 1 = Cys Cys384Gly 0.030 1.00 G 2 = Gly 0.970

Part II: Analysis of Locally Genotyped SNPs

Local SNP Genotyping—Eight additional. SNPs on Chromosome 10 overlyingthree susceptibility genes, PLEKHA1 (rs12258692, rs4405249 andrs1045216), LOC387715 (rs10490923, rs2736911, rs10490924) and PRSS11(rs11538141, rs1803403) were genotyped. This genotyping effort includedall of the non-synonymous SNPs that have been reported for these genesin the NCBI databases (see FIG. 1). As part of another study (Conley etal. 2005 Candidate gene analysis suggests a role for fatty acidbiosynthesis and regulation of the complement system in the etiology ofage-related maculopathy), two CFH variants (rs10922093 and rs1061170)were genotyped, which, used here as well. Genotyping of additional SNPsunder the GRK5/RGS10 locus is in process. Genotype data for rs12258692,rs1803403 and the newly characterized SNP, rs4405249, one base 3′ tors12258692, was collected by sequencing (Rexagen Corporation, Seattle,Wash.) and analyzed using Sequencher software (Gene Codes Corporation,Ann Arbor, Mich.). Genotype data for rs11538141, rs2736911, rs10490923and rs10490924 was collected using RFLP. The primers, amplificationconditions and restriction endonucleases, where appropriate, can befound in Table 4 for SNPs that were genotyped by sequencing or RFLP.

TABLE 4 Primers, annealing conditions and restriction endonucleases usedfor genotype data collection for those genotyped via sequencing or RFLP.NA = not applicable Annealing SEQ ID Temp Restriction Variant PrimerSequences NO: (° C.) Enzyme rs11538141 CAG AGT CGC CAT GCA GAT CC (F) 158 MnlI CCC GAA GGG CAC CAC GCA CT (R) 2 rs2736911 GCA CCT TTG TCA CCACAT TA (F) 3 54 DraIII GCC TGA TCA TCT GCA TTT CT (R) 4 rs10490923 GCACCT TTG TCA CCA CAT TA (F) 5 54 HhaI GCC TGA TCA TCT GCA TTT CT (R) 6rs10490924 GCA CCT TTG TCA CCA CAT TA (F) 7 54 PvuII GCC TGA TCA TCT GCATTT CT (R) 8 rs1803403 TGC TGT CCC TTT GTT GTC TC (F) 9 55 NA AGA CACAGA CAC GCA TCC TG (R) 10 rs12258692 GCC AGG AAA AGG AAC CTC (F) 11 54NA GCC AGG CAT CAA GTC AGA (R) 12

Genotype data for rs1045216 was collected using a 5′ exonucleaseAssay-on-Demand TaqMan assay (Applied Biosystems Incorporated, La Jolla,Calif.). Amplification and genotype assignments were conducted using theABI7000 and SDS 2.0 software (Applied Biosystems). Two unrelated CEPHsamples were genotyped for each variant and included on each gel and ineach TaqMan tray to assure internal consistency in genotype calls.Additionally, double-masked genotyping assignments were made for eachvariant, compared, and each discrepancy addressed using raw data orre-genotyping. See Table 3 for the correspondence between allele labelsprovided herein and the actual alleles, and, for non-synonymous SNPs,the amino acid change.

Part III: Interaction and Odds Ratio (OR) Analysis

Unrelated cases—No unrelated cases were genotyped by CIDR, but 196unrelated cases were genotyped locally for additional SNPs. Forcomputation of odds ratios and for interaction analyses (see below), aset of unrelated cases were generated by drawing one “Type A” affectedperson from each family. 321 locally-genotyped families had at least one“Type A” affected person. If a family had more than one “A” affectedperson, the person that was genotyped the most in rs800292 (CFH),rs1061170 (CFH), rs1537576 (GRK5) and rs4146894 (PLEKHA1) was chosen; ifthe number of genotyped SNPs did not distinguish between two persons,the person who developed the disease younger was chosen, and otherwise‘A’ affected cases were drawn at random from the persons genotyped themost and with the earliest age of onset. 577 CIDR families had at leastone “A” affected person, 321 of these families were also genotypedlocally, and the ‘A’ affected person was chosen to be the same as forthe local set. For the remaining 256 families, selection was based onthe same criteria as above except only rs800292 (CFH), rs1537576 (GRK5),and rs4146894 (PLEKHA1) were used to find the person with the mostcomplete genotyping.

Analysis of Interaction with CFH

Possible interaction between CFH on Chromosome 1 and the genes onChromosome 10 were investigated by testing with the GIST if SNPs in CFHcontributed to the linkage signal on Chromosome 10 and SNPs onChromosome 10 contributed to the linkage signal on Chromosome 1. Thiswas done by using weights from SNPs on one chromosome and family-basedNPLs from the other.

Logistic regression also was used to evaluate different interactionmodels and test for interaction following the approach described byNorth et al. (2005) Application of logistic regression to case-controlassociation studies involving two causative loci. Hum Hered 59:79-87. Inthis approach, many different possible models of the interactions,allowing simultaneously for additive and dominant effects at both of theloci are fit, and relative likelihoods of the different models arecompared in order to draw inferences about the most likely andparsimonious model. As previously described (North et al. 2005), themodels fit include a MEAN model in which only the mean term isestimated, ADD1, ADD2 and ADD models which assume an additive effect atone or other or both loci, DOM1, DOM2 and DOM models which additionallyincorporate dominance effects and two further models, ADDINT and DOMINT,which allow for interactive effects (For more detail, please see Northet al. (2005)). Since some pairs of these models are not nested, theywere compared using the Akaike information criteria (AIC); in thisapproach, the model with the lowest AIC is considered the best fittingand the most parsimonious. For these analyses, the program provided byNorth and his colleagues was used. After some bugs that we discoveredhad been fixed; the results were double-checked with our own R program.To maximize the sample size, CIDR SNPs in high LD with a highlysignificant non-synonymous SNP within each gene were chosen. The CIDRSNP rs800292 was chosen to represent rs10611710 (Y402H variant of CFH),and the CIDR SNP rs4146894 represents rs1045216 in PLEKHA1. Similarly, arepresentative CIDR SNP in GRK5, RGS10, and PRSS11 was selected.

Magnitude of Association

Crude odds ratios was calculated and attributable risk for SNPs wasestimated in each gene. The allele that was least frequent in thecontrols was considered to be the risk allele. Attributable risk wasestimated using the formula AF=100*P*(OR−1)/(1+P*(OR−1), where OR is theodds ratio and P is the frequency of the risk allele in the population,as estimated from the controls. This was done using ‘Type A’ affectedscompared to controls, subjects with CNV compared to controls andsubjects with GA compared to controls. To use maximum possible samplesize different, but overlapping, samples for CIDR and locally typed SNPswere used. 577 cases drawn from the families and 179 unrelated caseswere used for calculating OR and AR of CIDR SNPs but 517 cases (of whom321 are within the 577 CIDR SNP cases) and 117 controls (all within the179 CIDR SNP controls) for calculating the OR and AR on the locallygenotyped SNPs.

Multiple Testing Issues

In view of very strong evidence from previous studies that there is anARM-susceptibility locus in the Chromosome 10q26 region, the analysesperformed here are aimed at estimating the location of thesusceptibility gene, rather than hypothesis testing. Multiple testingissues are most crucial and relevant in the context of hypothesistesting. In estimation, the focus of these studies is simply where thesignal is the strongest. In any event, any correction for multipletesting would not alter the rank order of the results. A Bonferronicorrection, which does not account for any correlation between tests dueto LD, for 196 tests at the 0.05 level would lead to a significancethreshold of 0.05/196=0.00026; correlations due to LD would lead to alarger threshold.

Results

Analyses were carried out in three sequential steps. First, the set ofdata that had been genotyped at CIDR was analyzed. Second, after locallygenotyping 8 additional SNPs in the PLEKHA1/LOC387715/PRSS11 region onChromosome 10, we then analyzed the locally-genotyped data set wasanalyzed. Allele frequency estimation, testing for Hardy-Weinbergequilibrium (Table 3), CCREL association testing, and GIST testing wascarried out on both of these (overlapping) data sets as described above.Third, interaction between the Chromosome 1 and Chromosome 10 regionswas analyzed and whether or not the risk differed as a function of thepresence of either geographic atrophy or choroidal neovascular membraneswas examined.

Part I: Analysis of CIDR SNPs CIDR Linkage Results

Using the CIDR SNPs and applying the same linkage analysis approaches asin our previous studies (Weeks et al. 2004), the peak of the Sallmultipoint curve on Chromosome 10 implicates the GRK5 region (‘G’ inFIG. 2; rs1537576 in GRK5 had single-point Sall of 1.87 while themaximum single-point Sall of 3.86 occurred at rs555938, 206 kbcentromeric of GRK5), but several elevated two-point non-parametric SallLOD scores and our highest heterogeneity LOD score (HLOD) that drawattention to the PLEKHA1/LOC387715/PRSS11 region (“P” in FIG. 2). Inthis region, SNP rs4146894 in PLEKHA1 had a two-point Sall of 3.34 andthe highest two-point HLOD of 2.66, while SNPs rs760336 and rs763720 inPRSS11 had two-point Sall's of 2.69 and 2.23 respectively. However, thesupport interval is large (10.06 cM, FIG. 2), and so localization fromthe linkage analyses alone is rather imprecise.

The effect of failing to take SNP-SNP LD into account was explored bycomparing the multipoint scores with all SNPs (FIG. 2-1) to thosecomputed with only the htSNPs (FIG. 2-2). Two of the peaks almost vanishcompletely (referred to as false peaks, “F”s, in FIG. 2, left panel)when only using H-clust SNPs; interestingly these two peaks lie withinhaplotype blocks (FIGS. 3A and 3B) while the LD around the highestmulti- and two-point LOD scores is low (FIGS. 3C, 3D-1, 3D-2, 3D-3 and3D-4]), indicating the importance of taking LD into account whenperforming linkage analysis.

Linkage results on Chromosome 1 gave three peaks with Sall greater than2, only one of those peaks was observed when analysis was restricted tohtSNPs (FIG. 4). This remaining peak overlies the complement factor H(CFH) gene and includes two SNPs with very high two-point Sall and HLODscores; rs800292, a non-synonymous SNP in CFH, had a Sall of 1.53 and aHLOD of 2.11, while SNP rs1853883, 165 kb telomeric of CFH, had a Sallof 4.06 and a HLOD of 3.49. These results strongly support earlierfindings of CFH's involvement in ARM (Conley et al. 2005; Edwards et al.2005; Hageman et al. 2005b A common haplotype in the complementregulatory gene factor H (HF1/CFH) predisposes individuals toage-related macular degeneration. Proc Natl Acad Sci USA.; Haines et al.2005; Klein et al. 2005; Zareparsi et al. 2005a). The vanishing peaks(‘F’s in FIG. 4-1) we saw when using all of our SNPs in the linkageanalysis are located within strong haplotype blocks (FIGS. 5A and 5B),while the LD under the CFH peak is relatively low (FIG. 5C).

CIDR Association Results

For finer localization than can be obtained by linkage, associationanalyses was employed (which were very successful in discovering CFH onChromosome 1). Here, association analyses was performed using the CCRELapproach, which permits one to simultaneously use our unrelated controlsand all of our related familial cases by appropriately adjusting for therelatedness of the cases. In the CIDR sample on chromosome 10, withinour linkage peak, we found a cluster of four adjacent SNPs wasidentified having very small p-values (rs4146894, rs1882907, rs760336and rs763720) that overlies three genes: PLEKHA1, LOC387715 and PRSS11.The strongest CCREL results on chromosome 10 were in PLEKHA1 with SNPrs4146894 (Table 5). The moving window haplotype analyses using threeSNPs at a time (“haplo3”) results in very small p-values across thewhole PLEKHA1 to PRSS11 region (Table 5). The association testing alsogenerates some moderately small p-values in the GRK5 region, which iswhere the highest evidence of linkage occurs.

The CCREL was performed on 56 SNPs spanning the linkage peak onChromosome 1 and found two highly significant SNPs (rs800292 andrs1853883) that overlie CFH (Table 5). The moving window haplotypeanalysis using two (“haplo2”) and three (“haplo3”) SNPs at a timeresults in extremely low p-values across the whole CFH gene (Table 5)and again supporting earlier findings of strong association of CFH withARM.

CIDR GIST Results

When GIST testing is carried out on the CIDR data set, the two smallestp-values (0.006, 0.004) in Chromosome 10q26 occur in the GRK5/RGS10region, while the third smallest p-value (0.008) occurs in PLEKHA1(Table 5). All four SNPs in the GRK5 gene have small GIST p-values. TheGIST results suggest that both GRK5 and PLEKHA1 contribute significantlyto the linkage signal on Chromosome 10, and CFH to the linkage signal onChromosome 1. Neither of the two SNPs in PRSS11 contributessignificantly to the linkage signal on Chromosome 10. There was noevidence that these two genes on Chromosome 10 were related to thelinkage signal seen on Chromosome 1.

PART II: Analysis of Locally Genotyped SNPs Local Association Results

After typing additional SNPs locally, the allele and genotype testgenerate extremely small p-values in each of the three genesPLEKHA1/LOC387715/PRSS11 (Table 6). The moving window haplotype analysesusing three SNPs at a time (“haplo3”) results in very small p-valuesacross the whole PLEKHA1/LOC387715/PRSS11 region (Table 6). Thus, whileassociation implicates the PLEKHA1/LOC387715/PRSS11 region, it does notdistinguish among these genes.

TABLE 5 CCREL, GIST, and allele frequency estimation on families (594)and controls (179) typed at CIDR. GENE SNP families controls allele_testhaplo2 haplo3 geno_test GIST (NPL 10) GIST (NPL 1) rs6658788 0.460 0.4890.28475 0.01470 0.00775 0.35658 0.106 0.055 rs1538687 0.234 0.3070.00141 0.00204 0.00675 0.00424 0.781 0.129 rs1416962 0.321 0.3520.13492 0.34246 0.36032 0.32841 0.566 0.019 rs946755 0.317 0.344 0.168120.16876 <0.00001    0.32938 0.513 0.012 rs6428352 0.001 0.003 1.00000<0.00001    <0.00001    1.00000 CFH rs800292 0.132 0.232 <0.00001   <0.00001    <0.00001    <0.00001    0.437 0.001 CFH rs70620 0.147 0.1730.19864 <0.00001    <0.00001    0.42335 0.893 0.333 rs1853883 0.6300.489 <0.00001    <0.00001    <0.00001    <0.00001    0.521 <0.001   rs1360558 0.425 0.397 0.42543 0.67049 0.01558 0.71496 0.183 0.296rs955927 0.416 0.391 0.43909 0.01146 0.03400 0.73224 0.065 0.145rs4350226 0.055 0.095 0.00250 0.00988 0.00964 0.00266 0.171 0.242 GRK5rs4752266 0.220 0.223 0.78366 0.26028 0.29913 0.04848 0.088 0.475 GRK5rs915394 0.214 0.187 0.12637 0.17970 0.00350 0.31197 0.028 0.643 GRK5rs1268947 0.112 0.117 0.89140 0.01174 0.01398 0.96369 0.052 0.345 GRK5rs1537576 0.507 0.433 0.02286 0.01822 0.02755 0.04309 0.006 0.251rs2039488 0.078 0.115 0.01603 0.09338 0.12306 0.06163 0.004 0.609 RGS10rs1467813 0.286 0.293 0.73004 0.77610 0.89538 0.81737 0.539 0.582rs927427 0.514 0.464 0.05480 0.00002 0.00001 0.05244 0.198 0.577 PLEKHA1rs4146894 0.598 0.466 <0.00001    <0.00001    <0.00001    <0.00001   0.008 0.802 rs1882907 0.127 0.187 0.00264 0.00009 0.00004 0.00513 0.1690.172 PRSS11 rs760336 0.395 0.480 0.00280 0.00075 0.00089 0.01281 0.2320.581 PRSS11 rs763720 0.295 0.212 0.00043 0.00059 0.00337 0.00248 0.1980.021 Frequency of minor allele in the controls is reported for bothcontrols (estimated by counting) and families (estimated by Mendelversion 5), the allele frequency is bolded if the allele frequencydiffers between controls and families by more than 0.1. P-values forallele test, haplotype 2 SNP moving window test, haplotype 3 SNP movingwindow test and genotype test from the CCREL are bolded if ≦0.05 andbolded and underlined if ≦0.001. GIST P-values using NPL scores fromChromosome 1 and 10 are reported and bolded if less than 0.05, andbolded and underlined if ≦0.001.

TABLE 6 CCREL, GIST, and allele frequency estimation on locally-typedfamilies (323) and controls (117). GENE SNP families controlsallele_test haplo2 haplo3 geno_test GIST (NPL 10) GIST (NPL 1) rs66587880.563 0.483 0.02200 0.00052 0.00162 0.04920 0.319 0.244 rs1538687 0.2130.342 0.00004 0.00043 0.00066 0.00014 0.652 0.302 rs1416962 0.299 0.3930.00597 0.02623 0.02051 0.01819 0.442 0.041 rs946755 0.295 0.380 0.012340.01243 <0.00001    0.04531 0.409 0.040 rs6428352 0.001 0.004 1.00000<0.00001    <0.00001    1.00000 CFH rs800292 0.120 0.269 <0.00001   <0.00001    <0.00001    <0.00001    0.315 0.014 CFH rs1061170 0.6090.310 <0.00001    <0.00001    <0.00001    <0.00001    0.895 0.132 CFHrs10922093 0.210 0.295 0.00693 0.00175 <0.00001    0.01723 0.360 0.327CFH rs70620 0.148 0.150 0.91163 <0.00001    <0.00001    0.56770 0.7370.356 rs1853883 0.633 0.432 <0.00001    <0.00001    <0.00001 <0.00001   0.776 0.011 rs1360558 0.437 0.389 0.18014 0.43576 0.02079 0.37993 0.9750.488 rs955927 0.433 0.385 0.15343 0.01037 — 0.36087 0.017 0.585rs4350226 0.050 0.103 0.00312 — — 0.00373 0.228 0.174 GRK5 rs47522660.223 0.226 0.81772 0.27748 0.64917 0.08279 0.107 0.453 GRK5 rs9153940.228 0.209 0.34489 0.83219 0.05560 0.62183 0.049 0.320 GRK5 rs12689470.117 0.115 0.81975 0.02748 0.02192 0.78965 0.049 0.689 GRK5 rs15375760.497 0.419 0.02604 0.02232 0.05636 0.06334 0.012 0.023 rs2039488 0.0830.115 0.11177 0.42428 — 0.42399 0.025 0.358 RGS10 rs1467813 0.293 0.2950.86608 — — 0.85954 0.506 0.492 rs927427 0.506 0.487 0.56710 0.000560.00083 0.42264 0.306 0.625 PLEKHA1 rs4146894 0.611 0.474 0.000040.00012 0.00053 0.00024 0.006 0.737 PLEKHA1 rs12258692 0.008 0.0001.00000 0.54750 0.00018 1.00000 PLEKHA1 rs4405249 0.139 0.158 0.393780.00026 0.00280 0.33118 0.003 0.345 PLEKHA1 rs1045216 0.289 0.4270.00004 0.00036 0.00001 0.00026 0.068 0.825 rs1882907 0.131 0.1840.01761 0.00140 0.01099 0.04401 0.017 0.372 LOC387715 rs10490923 0.0890.141 0.02112 0.05024 <0.00001    0.03415 0.086 0.251 LOC387715rs2736911 0.121 0.119 0.71668 <0.00001    <0.00001    0.64230 0.3120.968 LOC387715 rs10490924 0.475 0.193 <0.00001    <0.00001    <0.00001   <0.00001    0.018 0.327 PRSS11 rs11538141 0.004 0.005 1.00000 0.007260.01676 1.00000 PRSS11 rs760336 0.373 0.474 0.00527 0.01386 0.000360.01396 0.479 0.683 PRSS11 rs763720 0.296 0.226 0.01645 0.00016 0.038990.305 0.451 PRSS11 rs1803403 0.118 0.030 0.00009 0.00022 0.714 0.778 Thefrequency of minor allele in the controls is reported for both controls(estimated by counting) and families (estimated by Mendel version 5),the allele frequency is bolded if the allele frequency differs betweencontrols and families by more than 0.1. P-values for allele test,haplotype 2 SNP moving window test, haplotype 3 SNP moving window testand genotype test from the CCREL are bolded if <= 0.05 and bolded andunderlined if <= 0.001. GIST P-values using NPL scores from chromosome 1and 10 are reported and bolded if less than 0.05. SNPs in italics arethe locally typed SNPs.

Local GIST Results

Of the three genes PLEKHA1/LOC387715/PRSS11, GIST most stronglyimplicates PLEKHA1 (Table 6). It also generates a small p-value inLOC387715 (rs10490924), but this SNP is in high LD with the PLEKHA1 SNPs(see FIG. 6A). When the locally-typed data set is used, GIST does notgenerate any significant results in PRSS11, similar to thenon-significant results observed above in the larger CIDR sample. Thisimplies that PLEKHA1 (or a locus in strong LD with it) is the mostlikely to be involved in AMD, and therefore LOC387715 remains apossibility.

For a fair assessment of which SNP accounts for the linkage signalacross the region, the NPLs were computed using only thelocally-genotyped families. This permits comparison of thePLEKHA1/LOC387715/PRSS11 results in Table 6 directly to the GRK5/RGS10results. On the locally-typed data set, the GRK5 GIST results are alsointeresting, with modestly smallish p-values of the same magnitude asthe p-values obtained from applying GIST to CFH (Table 6). However, notethat the p-values are not as small as those seen when the CIDR data setwas analyzed. Since all of the SNPs in the GRK5 region are CIDR SNPs,this difference is solely a function of sample size, as thelocally-typed data set is smaller than the CIDR data set (See Table 2).

Part III: Interaction and Odds Ratio (OR) Analyses GIST Results

No strong evidence of an interaction between the Chromosome 1 andChromosome 10 regions were seen with the GIST test. When using the CIDRdata set, to test if SNPs on chromosome 10 contribute to the linkagesignal on Chromosome 1 (Table 5 ‘GIST (NPL 1)’), only rs763720 in PRSS11gives p-value less than 0.05, however rs763720 does not contributesignificantly to the linkage signal on Chromosome 10, making thisp-value less convincing. When the local data set only was used, one GRK5variant (rs1537576), which was not significant in the larger CIDR dataset, gives p-value less than 0.05. Similarly, no evidence was seen thatSNPs within CFH contribute to the linkage signal on Chromosome 10, onlyone SNP (rs955927) gives p-value less than 0.05, this SNP is however notin CFH and not in strong LD (see FIG. 6B) with any SNPs in the CFH gene.

Logistic Regression Results

The logistic regression suggests that an additive model including bothvariants from CFH and PLEKHA1 is the best model for predictingcase-control status; this indicates that both genes are important to theARM phenotype. The AIC criteria also gives that an additive modelincluding an additive interaction term is the next best model (Table 7),however the interaction term is not significant (p-value=0.71). Similarresults were obtained for interaction between CFH and PRSS11, whereadditive model including both variants appears to be the best model.Within the GRK5/RGS10 region, a model with the CFH SNP alone is the bestfitting model, suggesting that the prediction of case-control statuswith CFH genotype does not improve by adding either the GRK5 or RGS10variant to the model.

TABLE 7 Results of fitting two-locus models by logistic regression.Locus 1: rs800292 (CFH) Locus 2 Model AIC AIC Diff rs1537576 MEAN 822.523.65 (GRK5) ADD1 798.8 0 ADD2 821.2 22.35 ADD 799.1 0.26 DOM1 799.70.91 DOM2 820.1 21.24 DOM 799.2 0.37 ADDINT 800.9 2.07 ADDDOM 802.1 3.25DOMINT 803.9 5.07 rs1467813 MEAN 821.9 23.53 (RGS10) ADD1 798.4 0 ADD2823.6 25.25 ADD 800.3 1.92 DOM1 799.3 0.91 DOM2 825.2 26.79 DOM 802.64.23 ADDINT 801.3 2.93 ADDDOM 804.9 6.54 DOMINT 805.2 6.83 rs4146894MEAN 823.02 49.26 (PLEKHA1) ADD1 799.24 25.49 ADD2 801.47 27.71 ADD773.76 0 DOM1 800.16 26.41 DOM2 803.44 29.68 DOM 776.44 2.68 ADDINT775.62 1.87 ADDDOM 779.85 6.09 DOMINT 778.26 4.5 rs760336 MEAN 821.927.32 (PRSS11) ADD1 798.4 3.78 ADD2 817.1 22.54 ADD 794.6 0 DOM1 799.34.69 DOM2 819 24.37 DOM 796.7 2.14 ADDINT 796 1.43 ADDDOM 802.1 7.46DOMINT 803.4 8.75 AIC of each model, and difference of the AIC from thebest fitting model. Model definitions are in the text.

Odds Ratios and Attributable Risk

The magnitude of association was estimated by calculating odds ratio(OR) and attributable risk (AR); the observed significant associations(Table 8) were consistent with the results from the CCREL tests in partsI and II. The two most significant SNPs in the PLEKHA1/LOC387715 regionoccur at SNPs rs4146894 (PLEKHA1) and rs10490924 (LOC387715); these twotests are highly correlated since the LD between those SNPs is very high(D′=0.93) (see FIG. 6A). The third most significant SNP (rs1045216) inthe Chromosome 10 region is a non-synonymous SNP in PLEKHA1 and in highLD with both rs4146894 (D′=97) and rs10490924 (D′=0.91).

TABLE 8 Odds ratios (OR), attributable risks (AR) and simulated p-valuesfrom a chi-squared test using 10000 replicates. HeterozygotesHomozygotes Dominant ((RR + RN) vs NN) (RN vs NN) Recessive (RR vs (RN +NN)) (RN vs NN) GENE SNP.allele OR 95% CI AR p-value OR AR OR 95% CI ARp-value OR AR rs6658788.2 0.83 0.57 1.22 −14.04 0.3909 1.09  2.69 1.010.68 1.5  0.21 1 0.88  −5.92 rs1538687.2 0.68 0.49 0.95 −19.38 0.0230.5  −11.74 0.42 0.23 0.78 −6.52 0.0068 0.38 −12.42 rs1416962.2 0.84 0.61.18 −10.02 0.3418 0.89  −2.57 0.82 0.49 1.38 −2.31 0.5002 0.77  −5.74rs946755.2 0.8  0.57 1.13 −12.52 0.232 1    0.04 0.9  0.53 1.52 −1.240.7816 0.81  −4.34 rs6428352.2 — — — — — — — — — — — — — — CFHrs800292.1 0.43 0.3 0.62 −30.01 <0.0001 0.48 −23.85 0.15 0.05 0.45 −4.980.0001 0.12   −8.19 CFH rs1061170.2 5.29 3.35 8.35 68.2 <0.0001 2.6628.55 4.57 2.48 8.42 30.06 <0.0001 10.05     63.72 CFH rs10922093.1 0.590.39 0.88 −25.61 0.0111 0.63 −19.65 0.5  0.24 1.04 −4.98 0.0736 0.41−10.14 CFH rs70620.1 0.83 0.57 1.19  −5.64 0.3366 0.85  −4.29 0.67 0.271.68 −1.3  0.4525 0.64  −1.93 rs1853883.2 2.67 1.78 4.01 54.41 <0.00011.65  19.21 2.08 1.43 3.02 22.06 0.0003 3.55   55.04 rs1360558.1 1.160.82 1.65  9.12 0.414 1.1   5.39 1.25 0.8 1.96  3.94 0.3774 1.32  9.01rs955927.2 1.13 0.79 1.6  7.5 0.5303 1.28  6.35 1.31 0.83 2.08  4.530.2588 1.36  9.38 rs4350226.2 0.51 0.32 0.81  −9.68 0.0038 0.27  −4.760.16 0.01 1.74 −0.95 0.142 0.14  −1.16 GRK5 rs4752266.2 0.88 0.62 1.23 −5.57 0.4325 3.27  10.68 2.81 0.98 8.04  3.89 0.0457 2.56  5.51 GRK5rs915394.2 1.28 0.9 1.82  8.91 0.1543 1.35  2.73 1.56 0.58 4.14  1.530.3892 1.68  2.72 GRK5 rs1268947.2 1.05 0.7 1.57  1.06 0.841 1.24  1.821.27 0.35 4.55  0.45 0.7761 1.28  0.58 GRK5 rs1537576.2 1.59 1.11 2.29 27.95 0.0109 0.89  −3.74 1.08 0.71 1.62  1.59 0.7579 1.47  15.14rs2039488.2 0.7  0.45 1.07  −6.5 0.1067 0.23 −11.98 0.19 0.04 0.79 −2.330.0242 0.18 −2.85 RGS10 rs1467813.1 0.96 0.69 1.35  −1.84 0.8645 1.01 0.42 0.77 0.42 1.38 −2.27 0.4265 0.77  −3.76 rs927427.1 1.09 0.74 1.62 6.57 0.6172 0.94  −4.66 1.67 1.09 2.56 10.73 0.0201 1.6   19.91 PLEKHA1rs4146894.1 2.22 1.49 3.31   46.78 0.0002 1.77  33.08 2.21 1.49 3.2920.46 <0.0001 3.31   49.88 PLEKHA1 rs12258692.2 — — — — — — — — — — — —— — PLEKHA1 rs4405249.1 0.62 0.33 1.15 −12.96 0.1692 0.61 −12.69 0.870.1 7.56 −0.23 1 0.77  −0.57 PLEKHA1 rs1045216.2 0.48 0.32 0.74 −51.230.0005 0.49 −18.27 0.37 0.21 0.65 −14.3   0.0003 0.28 −35.68 rs1882907.20.58 0.4 0.84 −16.73 0.0026 0.44  −5.79 0.31 0.1 0.97 −2.37 0.0438 0.27 −3.65 LOC387715 rs10490923.2 0.53 0.31 0.9 −13.27 0.0239 0.34  −9.010.22 0.04 1.09 −2.51 0.0809 0.2   −3.32 LOC387715 rs2736911.2 0.72 0.421.21  −6.92 0.2552 1.47  1.99 1.1  0.13 9.53 0.1 1 1.03  0.04 LOC387715rs10490924.2 5.03 3.2 7.91   57.11 <0.0001 2.72  22.76 5.75 2.46 13.4621.2   <0.0001 10.57     42.71 PRSS11 rs11538141.2 — — — — — — — — — — —— — — PRSS11 rs760336.2 0.64 0.44 0.93 −35.37 0.013 0.8   −6.95 0.690.46 1.03 −7.95 0.0773 0.55 −26.43 PRSS11 rs763720.1 1.69 1.2 2.38 21.24 0.0018 1.55  16.95 2.63 1.1 6.25  5.17 0.0277 3.16  10.14 PRSS11rs1803403.1 2.98 1.25 7.06  10.51 0.0093 2.98  10.51 — — — — — — — “TypeA” affecteds are compared to controls. OR and AR values are bolded andunderlined if corresponding p-values are less than 0.001 and bolded ifless than 0.05. SNP.allele denotes the SNP measured and the risk allele(minor allele in controls). RR denotes homozygotes for the risk allele,RN denotes heterozygotes for the risk allele and NN denotes homozygotesfor the normal allele. SNPs in italics are the locally typed SNPs.

We obtained similar results and similar OR and AR values (Table 8) asothers have reported for the CFH gene. The three most significant SNPswere rs1061170 (Y402H variant), rs800292 (in CFH) and rs1853883 (instrong LD with rs1061170, D′=91).

The magnitude of the association seen within PLEKHA1/LOC387715 is verysimilar to the level of association seen between CFH and ARM; both lociresult in extremely low p-values (p-values<0.0001). The OR and AR valueswere also similar, within CFH the dominant OR was 5.29 (95% CI3.35-8.35) and within PLEKHA1/LOC387715 it was 5.03 (95% CI 3.2-7.91),the dominant AR for CFH and PLEKHA1/LOC387715 was 68% and 57%,respectively.

Sub-Phenotype Analyses

We estimated odds ratios and attributable risk in cases with exudativedisease vs. controls, and cases with geographic atrophy versus controls(Table 9). Odds ratios and corresponding p-values yield similar findingsas the allele test of CCREL (Table 5 and 6). We found no majordifferences between the odds ratios for the presence of eithergeographic atrophy or choroidal neovascular membranes.

TABLE 9 OR and AR from analysis of ARM subtypes. Heterozygotes Dominant((RR + RN) vs NN) (RN vs NN) GENE SNP.allele Subtype OR 95% CI ARp-value OR AR rs6658788.2 CNV 0.84 0.56 1.25 −13.41 0.36706 1.21  6.19rs6658788.2 GA 0.88 0.55 1.4  −9.92 0.63064 1.07  2.18 rs1538687.2 CNV0.71 0.5 1.02 −17.04 0.07499 0.54 −10.68 rs1538687.2 GA 0.62 0.41 0.94−23.86 0.0317 0.56 −10.14 rs1416962.2 CNV 0.88 0.61 1.25 −7.7 0.416761.02  0.46 rs1416962.2 GA 0.77 0.51 1.17 −15.12 0.24708 0.69  −7.53rs946755.2 CNV 0.84 0.59 1.2  −9.81 0.37326 1.14  2.86 rs946755.2 GA0.73 0.48 1.11 −18.18 0.17258 0.79  −4.46 rs6428352.2 CNV — — — — — — —rs6428352.2 GA — — — — — — — CFH rs800292.1 CNV 0.48 0.33 0.7 −26.970.0002 0.53 −21.4  CFH rs800292.1 GA 0.39 0.25 0.62 −33.02 0.0002 0.44−26.29 CFH rs1061170.2 CNV 5.25 3.22 8.55 68    <0.0001 2.37  24.74 CFHrs1061170.2 GA 5.76 3.17 10.47   70.42 <0.0001 3.31  35.78 CFHrs10922093.1 CNV 0.56 0.37 0.85 −28.05 0.0083 0.61 −20.84 CFHrs10922093.1 GA 0.51 0.31 0.84 −32.2  0.0089 0.58 −23.06 CFH rs70620.1CNV 0.77 0.52 1.14  −7.45 0.23338 0.8  −5.9 CFH rs70620.1 GA 0.72 0.451.15  −9.48 0.18978 0.78 −6.4 rs1853883.2 CNV 2.52 1.64 3.89   52.140.0002 1.5   15.44 rs1853883.2 GA 3.54 1.97 6.36   64.51 <0.0001 1.95 25.93 rs1360558.1 CNV 1.1  0.76 1.59  5.96 0.64364 1.04  2.29rs1360558.1 GA 1.16 0.75 1.79  9.09 0.57904 1.13  6.6 rs955927.2 CNV1.12 0.78 1.63  7.31 0.51105 1.32  7.01 rs955927.2 GA 1.08 0.7 1.67 5 0.74163 1.18  4.02 rs4350226.2 CNV 0.55 0.34 0.91  −8.65 0.0209 — —rs4350226.2 GA 0.52 0.28 0.96  −9.46 0.0462 — — GRK5 rs4752266.2 CNV0.93 0.65 1.34  −2.87 0.71243 3.13  10.08 GRK5 rs4752266.2 GA 0.78 0.511.19 −10.33 0.27667 3.67  12.31 GRK5 rs915394.2 CNV 1.39 0.96 2.01 11.91 0.08469 1.28  2.23 GRK5 rs915394.2 GA 1.09 0.7 1.67  2.88 0.744931.38  2.94 GRK5 rs1268947.2 CNV 1.15 0.75 1.75  3.15 0.52415 1.23  1.72GRK5 rs1268947.2 GA 0.78 0.46 1.32 −5   0.42146 1.24  1.82 GRK5rs1537576.2 CNV 1.57 1.07 2.3 27.1 0.0211 0.88  −4.02 GRK5 rs1537576.2GA 1.84 1.15 2.94  35.48 0.0143 1.17  5.17 rs2039488.2 CNV 0.76 0.48 1.2 −5.05 0.28877 0.2  −12.5  rs2039488.2 GA 0.62 0.35 1.09 −8.3 0.115990.27 −11.29 RGS10 rs1467813.1 CNV 0.95 0.67 1.36  −2.31 0.85551 0.98 −0.68 RGS10 rs1467813.1 GA 0.85 0.56 1.29  −7.69 0.52905 0.88  −5.73rs927427.1 CNV 1.08 0.72 1.63  5.82 0.75722 0.91  −6.99 rs927427.1 GA1.1  0.68 1.78  6.67 0.81152 0.98  −1.63 PLEKHA1 rs4146894.1 CNV 2.531.64 3.91   52.45 <0.0001 1.94  37.72 PLEKHA1 rs4146894.1 GA 2.09 1.243.51 44   0.0069 1.77  33.08 PLEKHA1 rs12258692.2 CNV — — — — — — —PLEKHA1 rs12258692.1 GA — — — — — — — PLEKHA1 rs4405249.1 CNV 0.53 0.271.02 −16.43 0.06989 0.51 −16.36 PLEKHA1 rs4405249.1 GA 0.63 0.3 1.33−12.27 0.17898 0.64 −11.43 PLEKHA1 rs1045216.2 CNV 0.5  0.32 0.78 −48.350.0026 0.4  −22.18 PLEKHA1 rs1045216.2 GA 0.44 0.26 0.72 −58.72 0.0010.45 −19.85 rs1882907.2 CNV 0.52 0.35 0.77 −19.38 0.0024 0.7   −3.08rs1882907.2 GA 0.6  0.38 0.95 −15.9  0.035 0.22  −8.27 LOC387715rs10490923.2 CNV 0.48 0.28 0.85 −14.84 0.0114 0.17 −11.63 LOC387715rs10490923.2 GA 0.74 0.39 1.38  −7.07 0.41496 0.58  −5.49 LOC387715rs2736911.2 CNV 0.71 0.41 1.22  −7.12 0.24548 1.22  0.96 LOC387715rs2736911.2 GA 0.62 0.32 1.19  −9.43 0.13179 2.2   4.96 LOC387715rs10490924.2 CNV 5.64 3.52 9.06   60.52 <0.0001 2.81 23.7 LOC387715rs10490924.2 GA 3.43 2.02 5.84   44.55 <0.0001 2.63 21.83 PRSS11rs11538141.2 CNV — — — — — — — PRSS11 rs11538141.2 GA — — — — — — —PRSS11 rs760336.2 CNV 0.63 0.43 0.92 −37.33 0.0178 0.71 −10.35 PRSS11rs760336.2 GA 0.63 0.4 0.98 −36.73 0.0322 0.84  −5.52 PRSS11 rs763720.1CNV 1.77 1.24 2.54  23.25 0.0031 1.69  20.43 PRSS11 rs763720.1 GA 1.741.14 2.65 22.5 0.0107 1.4   12.86 PRSS11 rs1803403.1 CNV 3.33 1.39 8.02 12.17 0.0055 3.33  12.17 PRSS11 rs1803403.1 GA 3.85 1.53 9.72  14.490.0039 3.85  14.49 Homozygotes Recessive (RR vs (RN + NN)) (RR vs NN)GENE SNP.allele Subtype OR 95% CI AR p-value OR AR rs6658788.2 CNV 1.110.73 1.68 2.5 0.68123 0.95  −2.26 rs6658788.2 GA 1.01 0.62 1.66  0.35 10.92  −3.97 rs1538687.2 CNV 0.47 0.25 0.9 −5.97 0.0202 0.42 −11.38rs1538687.2 GA 0.45 0.2 1.01 −6.25 0.07239 0.38 −12.32 rs1416962.2 CNV0.95 0.56 1.62 −0.62 0.89111 0.89  −2.63 rs1416962.2 GA 0.62 0.31 1.24−5.07 0.22948 0.57 −11.11 rs946755.2 CNV 1.03 0.6 1.78  0.37 1 0.94 −1.39 rs946755.2 GA 0.69 0.34 1.38 −3.8  0.37606 0.6   −9.26rs6428352.2 CNV — — — — — — — rs6428352.2 GA — — — — — — — CFHrs800292.1 CNV 0.21 0.07 0.64 −4.59 0.0053 0.18  −7.64 CFH rs800292.1 GA0.09 0.01 0.75 −5.33 0.0113 0.08  −8.66 CFH rs1061170.2 CNV 4.11 2.27.69 27.24 <0.0001 9.35   61.82 CFH rs1061170.2 GA 5.66 2.9 11.04 35.95<0.0001 12.26   68.61 CFH rs10922093.1 CNV 0.4  0.18 0.91 −5.96 0.03270.33 −11.72 CFH rs10922093.1 GA 0.26 0.08 0.85 −7.43 0.032 0.21 −14.08CFH rs70620.1 CNV 0.63 0.24 1.69 −1.46 0.42256 0.6  −2.2 CFH rs70620.1GA 0.28 0.06 1.36 −2.9  0.17068 0.26 −4.1 rs1853883.2 CNV 1.88 1.28 2.7818.84 0.0014 3.2     51.28 rs1853883.2 GA 2.57 1.65 4 29.15 0.0003 5.12  66.42 rs1360558.1 CNV 1.24 0.78 1.98  3.75 0.41376 1.27  7.61rs1360558.1 GA 1.17 0.68 2.02  2.67 0.67873 1.25  7.14 rs955927.2 CNV1.34 0.83 2.17 4.9 0.20048 1.38  9.84 rs955927.2 GA 1.2  0.68 2.1  2.860.57564 1.22  6.06 rs4350226.2 CNV — — — — — — — rs4350226.2 GA — — — —— — — GRK5 rs4752266.2 CNV 2.82 0.96 8.24 3.9 0.06229 2.63  5.74 GRK5rs4752266.2 GA 2.88 0.9 9.23  4.04 0.06909 2.51  5.33 GRK5 rs915394.2CNV 1.56 0.57 4.3  1.54 0.48645 1.74  2.96 GRK5 rs915394.2 GA 1.42 0.444.58  1.17 0.57824 1.45  1.81 GRK5 rs1268947.2 CNV 1.35 0.36 5.05  0.580.76382 1.39  0.8 GRK5 rs1268947.2 GA 1   0.2 5.02 0   1 0.95 −0.1 GRK5rs1537576.2 CNV 1.06 0.69 1.63 1.3 0.83192 1.44  14.26 GRK5 rs1537576.2GA 1.44 0.89 2.34  8.51 0.17778 2.04  28.32 rs2039488.2 CNV 0.18 0.030.91 −2.36 0.0318 0.17  −2.88 rs2039488.2 GA 0.2  0.02 1.7 −2.3  0.217580.19  −2.83 RGS10 rs1467813.1 CNV 0.83 0.45 1.54 −1.61 0.63004 0.83 −2.79 RGS10 rs1467813.1 GA 0.81 0.39 1.69 −1.85 0.70453 0.76  −3.85rs927427.1 CNV 1.76 1.13 2.74 11.97 0.0107 1.65  21.15 rs927427.1 GA1.5  0.9 2.5  8.16 0.15618 1.47  16.43 PLEKHA1 rs4146894.1 CNV 2.46 1.633.71 23.64 <0.0001 3.95 56    PLEKHA1 rs4146894.1 GA 1.92 1.2 3.08 16.310.0084 2.87   44.63 PLEKHA1 rs12258692.2 CNV — — — — — — — PLEKHA1rs12258692.1 GA — — — — — — — PLEKHA1 rs4405249.1 CNV 0.97 0.11 8.85−0.05 1 0.83  −0.41 PLEKHA1 rs4405249.1 GA 0.57 0.04 9.31 −0.76 1 0.51−1.2 PLEKHA1 rs1045216.2 CNV 0.31 0.17 0.58 −15.72   0.0002 0.25 −38.01PLEKHA1 rs1045216.2 GA 0.32 0.15 0.7 −15.46  0.0017 0.24 −38.67rs1882907.2 CNV 0.44 0.14 1.38 −1.91 0.20808 0.38  −3.11 rs1882907.2 GA0.16 0.02 1.37 −2.89 0.12039 0.14  −4.32 LOC387715 rs10490923.2 CNV 0.1 0.01 0.98 −2.89 0.0413 0.09  −3.78 LOC387715 rs10490923.2 GA 0.48 0.082.91 −1.66 0.65244 0.45  −2.24 LOC387715 rs2736911.2 CNV 0.92 0.09 8.92−0.08 1 0.86  −0.18 LOC387715 rs2736911.2 GA 1.43 0.13 15.97  0.42 11.3   0.38 LOC387715 rs10490924.2 CNV 6.18 2.62 14.59 22.67 <0.000112.11     46.39 LOC387715 rs10490924.2 GA 4.74 1.9 11.84 17.47 0.00037.05   32.05 PRSS11 rs11538141.2 CNV — — — — — — — PRSS11 rs11538141.2GA — — — — — — — PRSS11 rs760336.2 CNV 0.61 0.39 0.95 −10.03  0.03480.49 −30.78 PRSS11 rs760336.2 GA 0.71 0.42 1.19 −7.3  0.23778 0.56−25.69 PRSS11 rs763720.1 CNV 2.1  0.85 5.18  3.55 0.12829 2.64  7.87PRSS11 rs763720.1 GA 4.71 1.88 11.79 11.06 0.0001 5.41   18.69 PRSS11rs1803403.1 CNV — — — — — — — PRSS11 rs1803403.1 GA — — — — — — — OR andAR are bolded and underlined if corresponding p-values (chi-squaredtest, p-values simulated using 10000 replicates) are less than 0.001 andbolded if less than 0.05. SNP.allele denotes the SNP measured and therisk allele (minor allele in controls). RR denotes homozygotes for therisk allele, RN denotes heterozygotes for the risk allele and NN denoteshomozygotes for the normal allele. SNPs in italics are the locally typedSNPs.

Discussion

Our linkage studies of ARM families have consistently identified theChromosome 1q31 and Chromosome 10q26 loci, in addition to several otherloci. Multiple linkage studies have replicated this finding and thus afocused SNP analysis of both regions using ARM families as well as withunrelated affected individuals and controls was undertaken. OnChromosome 1q31, we have confirmed the strong association with CFH thathas been reported by others (see also Conley et al. (2005)) and, for thefirst time, have shown that SNPs in CFH significantly account for thelinkage signal. Interestingly, our smallest GIST p-value (<0.001) iswith rs1853883, which has a high D′ of 0.91 with the Y402H variant, andnot with the presumed “disease-associated” Y402H variant itself. Thisraises the possibility that other possible ARM-related variants withinthe CFH gene may still have to be considered and that these may be inhigh LD with Y402H.

Our studies of Chromosome 10q26 have implicated two potential loci, avery strongly-implicated locus, inclusive of three tightly linked genes,PLEKHA1, LOC387715, and PRSS11, and a less strongly-implicated locuscomprising two genes, GRK5 and RGS10 (FIG. 1). The GIST analysis doesnot support PRSS11 as the ARM-related gene, but it does not completelyexclude it as a potential candidate. PLEHKA1 has the lowest GIST-derivedp-values while LOC387715 harbors the SNP with the strongest associationsignal and the highest odds ratios. With the high linkage disequilibriumbetween the SNPs in LOC387715 and PLEKHA1, one cannot clearlydistinguish between these genes from statistical analyses alone.However, it is clear that the magnitude of the impact of thePLEKHA1/LOC387715 locus on ARM is comparable to that which has beenobserved for the CFH locus. Like the recent studies in Science (Edwardset al. 2005; Haines et al. 2005; Klein et al. 2005), we have found inour case-control population that the CFH allele (either heterozygous orhomozygous) accounts for an odds ratio of 5.3 OR (CI: 3.4-8.4) and asignificant population attributable risk of 68%. In the same fashion,the high-risk allele within the PLEKHA1/LOC387715 locus accounts for anodds ratio of 5.0 (CI: 3.2-7.9) and 57% population attributable riskwhen considering both heterozygous and homozygous individuals. As notedby Klein et al (2005), the odds ratios that are determined from a casecontrol study will usually overestimate the equivalent relative riskthat is required for determining the lifetime risk.

In the case of Complement factor H (CFH) on Chromosome 1, theassociation data were extremely compelling for a single gene, eventhough CFH is within a region of related genes. In addition to theassociation data found by multiple independent groups, there isadditional biological data to implicate CFH, including localization ofthe protein within drusen deposits of ARM patients. Thus, we also mustconsider the biological relevance of the potential ARM-susceptibilitygenes identified by our studies of Chromosome 10q26.

As noted above, the GIST analysis most strongly implicates PLEKHA1,particularly when we included the additional non-synonymous SNPs that weadded to the genotyping. PLEKHA1 (GenBank NM_(—)001001974, NM_(—)021622,NP_(—)001001974 and NP_(—)067635; MIM 607772; UniGene Hs.287830) encodesthe protein, TAPP1, which is a 404 amino acid protein with a putativephosphatidylinositol 3,4,5-trisphosphate-binding motif (PPBM) as well astwo plectstrin homology (PH) domains. The last 3 C-terminal amino acidshave been predicted to interact with one or more of the 13 PDZ domainsof MUPP1 (similar to the PDZ domain within PRSS11). Dowler andcolleagues (Dowler et al. 2000 Identification ofpleckstrin-homology-domain-containing proteins with novelphosphoinositide-binding specificities. Biochem J 351:19-31.) have shownthat the entire TAPP1 protein as well as the C-terminal PH domaininteract specifically with phosphatidylinositol 3,4-bisphosphate(PtdIns(3,4)P2), but not with any other phosphoinositides. TAPP1, whichhas 58% identity with the first 300 amino acids of TAPP2, shows a 5-foldhigher affinity for PtdIns(3,4)P2 than TAPP2 and this binding is nearlyeliminated by mutation of the conserved arg212 to leucine within thePPBM region (which is part of the second PH domain). The mostwell-defined role for TAPP1 (and its relatives, Bam32 and TAPP2) hasbeen as an activator of lymphocytes. PtdIns(3,4)P2 is preferentiallyrecruited to cell membranes when lipid phosphatase (SHIP) is activatedalong with PI3Ks (phosphatidyl inositol 3-kinase). SHIP is responsiblefor the dephosphorylation of PIP3 to PtdIns(3,4)P2. SHIP is a negativeregulator of lymphocyte activation and thus TAPP1 (and TAPP2) may becrucial negative regulators of mitogenic signaling and of the PI3Ksignaling pathway. Thus, one can envision a role of PLEKHA1 and itsprotein TAPP1 in the eye by modifying local lymphocyte activation,consistent with the hypothesis that ARM is closely linked to aninflammatory process.

However, we need to still consider the biological plausibility of theother two candidate genes, LOC387715 and PRSS11, within this locus.Little is known regarding the biology of LOC387715 (Genbank XM 373477and XP 373477; UniGene Hs.120359), except that its expression appears tobe limited to the placenta. Our own reverse transcription experimentswith human retinal RNA have confirmed the expression of PLEKHA1 andPRSS11, but we have not detected LOC387715 transcripts in the retinaunder standard conditions, even though we confirmed its expression withplacental RNA (data not shown). However, we cannot exclude thepossibility that LOC387715 is expressed in very low levels in the retinaor retinal pigment epithelium or that its expression in non-oculartissues, such as dendritic cells or migrating macrophages, could be afactor in the pathogenesis of ARM.

PRSS11 (GenBank NM 002775 and NP 002766; MIM 602194 and UniGeneHs.501280) is one of the genes of the mammalian HtrA (high temperaturerequirement A) serine protease family, which has a highly conservedC-terminal PDZ domain (Oka et al. 2004 HtrA1 serine protease inhibitssignaling mediated by TGFfbeta family proteins. Development131:1041-1053). These secretory proteases were initially identifiedbecause of their homologies to bacterial forms that are required forsurvival at high temperatures and molecular chaperone activity at lowtemperatures. The ATP-independent serine protease activity is thought todegrade misfolded proteins at high temperature. The mammalian form,HtrA1, has been shown to be selectively stimulated by type III collagenalpha 1 C propeptide, in contrast to HtrA2. (Murwantoko et al. 2004Binding of proteins to the PDZ domain regulates proteolytic activity ofHtrA1 serine protease. Biochem J 381:895-904) Type IH collagen is amajor constituent (35-39% of the total collagen) in Bruch membrane andis also present in small amount in the retinal microvascular basementmembranes. Developmental studies have reported ubiquitous expression ofHtrA1 but with temporal and spatial specificities that coincide withthose regions in which TGF-beta proteins play a regulatory role. (DeLuca et al. 2004 Pattern of expression of HtrA1 during mousedevelopment. J Histochem Cytochem 52:1609-1617.) Oka and colleagues (Okaet al. 2004 HtrA1 serine protease inhibits signaling mediated by Tgfbetafamily proteins. Development 131:1041-1053.) have shown that HtrA1 iscapable of inhibiting signaling of a number of TGF-beta family proteins,including Bmp4, Bmp2 and TGF-beta1, presumably by preventing receptoractivation with a requirement for protease activity of the HtrA1molecule. One clue as to the potential importance of these relationshipsfor ARM comes from the studies of Hollborn et al (2004) Contrary effectsof cytokines on mRNAs of cell cycle- and ECM-related proteins in hRPEcells in vitro. Curr Eye Res 28:215-223 who found that human RPE cellsin vitro experienced reduced proliferation in the presence of TGF-beta1and TGF-beta2 and an increase in levels of collagen III and collagen IVtranscripts. Normally, a rise in collagen III would activate HtrA1 andlead to secondary inhibition of the effects of TGF-beta1. However, ifthe serine protease is less effective (either due to reduced synthesisor a nonfunctional mutation), then this regulatory pathway would bedisrupted, leading to an overall reduction of proliferation potential ofthe RPE cells, perhaps contributing to RPE atrophy or further changesthat could lead to the development of ARM. The gradual reduction insolubility of type III collagen in Bruch's membrane that has beenobserved with aging could also, in part, account for a general reductionin HtrA1 activity as an individual ages.

Both PRSS11 and PLEKHA1 are expressed in the retina, and a SAGE analysisof central and peripheral retina (GEO Expression data), indicates higherlevels of transcripts of both genes in the central macula (more so forPLEKHA1 than PRSS11). Multiple studies (reported in GEO profiles) haveshown that PLEKHA1 expression is significantly induced in a variety ofcell types in response to exposure to specific inflammatory cytokines.PRSS11 has also been investigated as part of a microarray expressionanalysis of dermal fibroblasts that have been oxidatively challenged ina comparison between normal and ARM patients. In that study, half of theARM samples (9/18) had lower Htra1 expression levels than any of thenormal samples. The lower levels of Htra1 in non-ocular tissues of ARMpatients would suggest that this is an intrinsic difference in thebiology of these patients as compared to normal individuals, and not aconsequence of degenerative changes in the eye.

The GRK5/RGS10 locus is supported by several lines of evidence. The peakof our S_(all) multipoint curve is directly over GRK5 and our largestsingle-point S_(all)=3.86 (rs555938) is only 206 kb centromeric to GRK5.The p-values for the GIST analysis of the GRK5/RGS10 CIDR data were0.004 and 0.006, which are even smaller than the p-value for the SNPwithin PLEKHA1 (0.008). Using our locally-genotyped sample, the GISTp-value for the GRK5 locus was 0.012, which is comparable to the p-valuethat we found for the Y402H variant in CFH (p=0.011). However, the CCRELanalyses were not very significant for the GRK5 SNPs and the odds ratioswere mostly non-significant.

Based on biological evidence, GRK5 (GenBank NM 005308 and NP 005299;UniGene Hs.524625; MIM 600870; and PharmGKB PA180) is reasonable ARMcandidate gene, given its role in modulating neutrophil responsivenessto chemoattractants and its interactions with the Toll 4 receptor(Haribabu and Snyderman 1993 Identification of additional members ofhuman G-protein-coupled receptor kinase multigene family. Proc Natl AcadSci USA 90:9398-9402; Fan and Malik 2003 Toll-like receptor-4 (TLR4)signaling augments chemokine-induced neutrophil migration by modulatingcell surface expression of chemokine receptors. Nat Med 9:315-321.),which has also been implicated in ARM (Zareparsi et al. 2005b Toll-likereceptor 4 variant D299G is associated with susceptibility toage-related macular degeneration. Hum Mol Genet 12:1449-55). The retinalor RPE expression of GRK5 is not especially relevant to the argument ofcausality because it would be the expression and function of GRK5 inmigrating lymphocytes and macrophages that would be crucial to its rolein the immune/inflammatory pathways that may be pathogenic in ARM. Thestrongest GIST results occur at rs2039488, which is actually locatedbetween GRK5 and RGS10, 3′ to the ends of both genes. Several other SNPswithin the GRK5 gene also have small GIST p-values, while the RGS10 SNPhas a non-significant GIST p-value. However, we cannot completelyexclude the possibility that there is a SNP within RGS10 that is instrong linkage disequilibrium with rs2039488.

RGS10 (GenBank NM 001005339, NM 002925, NP 001005339 and NP 002916;UniGene Hs.501200; and MIM 602856) is one of a family of G proteincoupled receptors that has been implicated in chemokine-inducedlymphocyte migration (Moratz et al. 2004 Regulation of chemokine-inducedlymphocyte migration by RGS proteins. Methods Enzymol 389:15-32.) andwhose expression in dendritic cells (which have been identified inARM-related drusen deposits) is modified by the Toll-like signalingpathway (Shi et al. 2004 Toll-like receptor signaling alters theexpression of regulator of G protein signaling proteins in dendriticcells: implications for G protein-coupled receptor signaling. J Immunol172:5175-5184). RGS10 and GRK5 expression in the same microarray studyof oxidatively-stressed dermal fibroblasts in AMD and control subjectsshowed minor fluctuations among the samples, but no clear differencesbetween the control and affected cases. This does not necessarily lowerthe potential for these genes being involved in ARM, since the dermalfibroblasts are lacking the cell populations that would be expected tohave modulation of RGS10- and/or GRK5-related proteins.

We have attempted to look at potential interactions between thehigh-risk alleles within the PLEKHA1/LOC387715 and GRK5/RGS10 loci withrespect to CFH on Chromosome 1. This is perhaps the first report to useGIST to examine these interactions and we found no evidence that the NPLdata on Chromosome 1 could be accounted for by the SNP data onChromosome 10. Conversely, we found no such associations between the NPLdata on Chromosome 10 and the SNP data from the CFH alleles. Logisticregression analysis also failed to identify an interaction, and itappears that a simple additive risk model is the most parsimonious. Wehave performed some initial logistic analyses that include exposure tosmoking. These analyses were initiated because of the previoussuggestion of an interaction of smoking with the biology of complementfactor H (Esparza-Gordillo et al. 2004 Genetic and environmental factorsinfluencing the human factor H plasma levels. Immunogenetics 56:77-82.)and our prior studies which found an interaction of smoking and thelocus on Chromosome 10q26 (Weeks et al. 2004). To date, we have found nostrong interaction of smoking with either the CFH or PLEKHA1/LOC387715loci, but we are still exploring a possible interaction with theGRK5/RGS10 locus and different modeling strategies. We also examined theassociations of ARM subphenotype with the SNPs on both Chromosomes 1 and10 (Table 9). We found no major differences in the odds ratios for thepresence of either geographic atrophy or choroidal neovascularmembranes, suggesting that these ARM loci contribute to a commonpathogenic pathway that can give rise to either end stage form of thedisease. This does not exclude the possibility that there are other, asyet undescribed, genetic loci that may confer specific risk togeographic atrophy or CNV development separately.

In summary, these SNP-based linkage and association studies illustrateboth the power and limitation of such methods to identify causativealleles and genes underlying ARM susceptibility. These geneticapproaches allow us to consider genes and their variants that maycontribute to a disease, whether or not there is tissue-specificexpression. Through high density SNP genotyping, we have narrowed thelist of candidate genes within the linkage peak found on Chromosome10q26, from hundreds to primarily GRK5 and PLEKHA1, but we cannotcompletely exclude the possible roles of RGS10 and/or PRSS11 andLOC387715. Additional genotyping of non-synonymous 3′ SNPs within theGRK5 gene may help to further discriminate between GRK5 and RGS10, butit may not establish a definitive assignment of causality. Replicationby other studies (such as in the case of CFH) may allow one to focus ona single gene, but there is also the distinct possibility that we willbe unable to achieve further resolution with association studies orclearly establish if there are more than two genes that are responsiblefor ARM susceptibility on Chromosome 10q26. However, molecularbiologists can now investigate the potential role of each of thesecandidate genes in mouse models of ARM and address the issue of a causalrole in disease pathogenesis.

Example 2 Follow-Up to Example 1

This Example provides additional data supporting and confirming theconclusions and discoveries provided in Example 1, in which allelicvariations in PLEKHA1 and hypothetical LOC387715 genes were identifiedas risk factors for Age-related maculopathy.

The etiology of ARM is complex, with environmental as well as geneticsusceptibility playing a role. Association-based analyses are generallymore sensitive to small genetic effects than linkage-based analyses andare extremely valuable for fine mapping of disease-related genes Cordellet al (2005) Genetic association studies Lancet. 366, 1121-1131.Case-control association studies with the use of unrelated individualsmay have advantages over family-based studies, especially when amultilocus genetic model is anticipated (Howson et al. (2005) Comparisonof population-and family-based methods for genetic association analysisin the presense of interacting loci. Genet Epidemiol. 29, 51-67. Rischet al. (2001) Implications of multilocus inheritance for gene-diseaseassociation studies. Theor Popul Biol. 60, 215-220.), however suchstudies are potentially sensitive to the ascertainment scheme for thecase and control cohorts. For this reason, there is value in assessingcandidate genes in populations with different ascertainment schemes.This Example investigates the complement factor H (CFH) gene, theelongation of very long chain fatty acid-like 4 (ELOVL4) gene, thePLEKHA1 gene, and the hypothetical LOC387715 gene in two distinctcohorts.

The association of the CFH gene with ARM susceptibility has beenestablished in samples of European American descent (Edwards et al.(2005), Haines et al. (2005), Klein et al. (2005), Hageman et al.(2005), Conley et al. (2005), Zareparsi et al. (2005) as well as insamples from the United Kingdom—Sepp, T. et al (2006) Complement factorH variant Y402H is a major risk detriment for geographic atrophy andchoroidal neovascularization in smokers and nonsmokers. InvestOphthalmol Vis Sci. 47, 536-540, Germany—Rivera et al (2005)Hypothetical LOC387715 is a second major susceptibility gene forage-related macular degeneration, contributing independently ofcomplement factor H to disease risk. Hum Mol Genet. 14, 3227-3236,France—Souied et al (2005) Y402H complement factor H polymorphismassociated with exudative age-related macular degeneration in the Frenchpopulation. Mol Vis. 11, 1135-1140, Iceland—Magnusson et al (2006) CFHY402H confers similar risk of soft drusen and both forms of advancedAMD. PLoS Med. 3, e5. and Japan—Okamoto et al (2006) Complement factor Hpolymorphisms in Japanese population with age-related maculardegeneration. Mo Vis. 12, 156-158.

Three studies support the PLEKHA1/LOC387715 locus on chromosome 10q26(Rivera et al (2005), Jakobsdottir Jr. et al (2005) and Schmidt et al(2006) Cigarette smoking strongly modifies the association of LOC387715and age-related macular degeneration Am J Hum Genet. 78, 852-864. Thestudy by Jakobsdottir et al. (2005) Susceptibility genes for age-relatedmaculopathy on chromosome 10q26. Am J Hum Genet. 77, 389-407 reportedthat the PLEKHA1/LOC387715 locus was significantly associated with ARMstatus, however strong linkage disequilibrium between PLEKHA1 andLOC387715 in the independent family-based and case-control populationsutilized for the study meant that a role for one gene over the othercould not be determined (Jakobsdottir, et al. (2005)). Since publicationof Jakobsdottir et al., evidence that the hypothetical LOC387715 genewas more likely to be the gene accounting for susceptibility to ARM hasbeen published in a study by Rivera et al. (2005) that utilized twoindependent case-control samples (Rivera et al. (2005)) and a study bySchmidt et al. that utilized both family based and case-control studiesSchmidt et al. All three studies indicated that the association of thisregion on chromosome 10q26 with ARM status was independent of theassociation with CFH that had been previously reported in all threepopulations (Haines et al. (2005, Conley et al. (2005), Rivera et al.(2005)). Additionally, based on the Schmidt et al. study, the effect ofthe LOC387715 locus appears to be modified by smoking history Schmidt etal. (2006).

Two studies have evaluated a potential role for ELOVL4 in ARM in humans.Ayyagari et al. (2001) Evaluation of the ELOVL4 gene in patients withage-related macular degeneration. Opthalmic Genet. 22, 233-239 evaluatedthe gene and found no significant association with ARM status in theirsporadic case-control analysis. However, Conley et al. found asignificant association of ELOVL4 and ARM status in our familial andsporadic case-control analyses Conley et al. (2005). The difference infindings between these studies may be related to the proportion of caseswith exudative ARM in each population, since Conley et al. found thatELOVL4 was especially associated with the exudative subphenotype. Theseresults indicate that additional studies are needed to establish orrefute a relationship between ELOVL4 and ARM.

The two cohorts utilized for this study were the Cardiovascular HealthStudy (CHS), a population-based cohort of individuals 65 years and olderat baseline for which ARM status was not a factor for ascertainmentFried et al. (1991) The Cardiovascular Health Study: design andrationale. Ann Epidemiol. 1, 263-276 and the Age-Related Eye DiseaseStudy (AREDS), a cohort of individuals aged 55 to 80 years participatingin a randomized controlled clinical trial of anti-oxidant and zincintervention for which ARM status was a factor for ascertainmentAge-Related Eye Disease Study Research Group (1999) the Age-Related EyeDisease Study (AREDS): design implications. AREDS report no. 1. ControlClin Trials. 20, 573-600. These cohorts have been previously described(Klein, R., et al. (2003) Early age-related maculopathy in thecardiovascular health study. Ophthalmology. 110, 25-33 and Age-RelatedEye Disease Study Research Group (2000) Risk factors associated withage-related macular degeneration. A case-control study in theage-related eye disease study: Age-Related Eye Disease Study ReportNumber 3. Ophthalmology. 107, 2224-2232)

This study was designed to evaluate the CFH, ELOVL4, PLEKHA1, andLOC387715 genes in two independent cohorts with very differentascertainment schemes in relation to ARM status and then to incorporatethe findings into meta-analyses. Association of a gene withsusceptibility to ARM regardless of ascertainment scheme would furtherincrease the evidence that the association is real and would enhance thelikelihood that evaluation of the gene(s) would accurately identify atrisk individuals.

Abbreviations: ARM=Age-related maculopathy; GA=Geographic atrophy;CNV=Choroidal neovascular membranes; OR=Odds ratio; PAR=Populationattributable risk; OR_(dom)=Odds ratio for dominance effects;OR_(rec)=Odds ratio for recessive effects; OR_(het)=Odds ratio forsubjects heterozygote for risk allele; and OR_(hom)=Odds ratio forsubjects homozygote for risk allele.

Material and Methods Cardiovascular Health Study (CHS)Participants—Sampling and Phenotyping

CHS is a population-based, longitudinal study primarily designed toidentify factors related to cardiovascular disease in those aged 65 andolder. Retinal assessments were performed at the 8 year follow up visitand surviving members of this cohort have just completed their 18 yearfollow up evaluation. Community-based recruitment took place in ForsythCounty, N.C.; Sacramento County, Calif.; Washington County, Md.; andPittsburgh, Pa. Medicare eligibility lists of the Health Care FinancingAdministration were utilized to identify individuals who were aged 65and older. Individuals aged 65 years and older living in the householdsof list members were also eligible. Inclusion criteria were minimal andincluded being non-institutionalized, expected to remain in the area forat least three years, able to give informed consent, notwheelchair-bound, not receiving hospice care, and not receivingradiation or chemotherapy for cancer. Fried et al. (1991). DNA samplesfrom the CHS were used for this research.

CHS subjects usually had the retina of one randomly selected eyephotographed and the photographs were graded by Dr. Gorin using the sameclassification model that was described in prior publications Weeks etal. (2004) Age-related maculopathy: a genomewide scan with continuedevidence of susceptibility loci within the 1q31, 10q26, and 17₈25regions. Am J Jum Genet. 75, 174-189. Only Caucasian individuals areincluded in the analysis, as the sample size of other groups with ARM istoo small for reasonable results: there were 182 black controls but only3 cases, and 5 controls of other races. All CHS cases (n=126) used foranalyses are “Type A”, which falls into our most stringent model forclinical classification Weeks et al. (2004). Individuals in thiscategory are clearly affected with ARM based on extensive and/orcoalescent drusen, pigmentary changes (including pigment epithelialdetachments), and/or the presence of end-stage disease [geographicatrophy (GA) and/or choroidal neovascular (CNV) membranes]. Very few CHScases had end stage ARM, GA or CNV (Table 10); therefore analyses ofspecific subtypes of ARM were not conducted. All CHS controls (n=1,051)were of AREDS grade 1. A few potential controls (n=22) had unclear signsof GA or CNV and were excluded from analyses.

TABLE 10 Characteristics of the study populations. Clinical subtypesMean (SD) age Neither GA only CNV only Both Total No. (%) males AREDSdata Controls (1) 76.53 (4.44) 175 — — — 175  86 (49) Cases (3-4-5)79.46 (5.23) 123 147 278 153 701 293 (42) Cases (4-5) 79.54 (5.23) 27147 278 153 605 253 (42) Cases (3) 78.93 (5.22) 96 0 0 0 96  40 (42)Cases (4) 78.83 (5.23) 24 59 149 34 266 124 (47) Cases (5) 80.10 (5.17)3 88 129 119 339 129 (38) CHS data Controls 70.27 (3.92) 1051 — — — 1051455 (43) Cases 73.22 (4.84) 100 15 9 2 126  55 (44) In the AREDS cohortmean age and phenotypic classification is based on age at last fundusphotography. The number in the parentheses denotes the disease severityaccording the AREDS grading method. In the CHS cohort mean age is basedon age at baseline visit, but retinal evaluation was done at 8-yearfollow-up visit.

Age-Related Eye Disease Study (AREDS) Participants—Sampling andPhenotyping

AREDS is a prospective, multicenter study of the natural history of ARMand age-related cataract with a clinical trial of high dose vitamin andmineral supplementation embedded within the study. Individuals recruitedinto the AREDS study were men and women aged 55 to 80 years atenrollment; these individuals were required to be free of any conditionor illness that would hinder long-term follow-up. Inclusion criteriawere minimal and included having ocular media clear enough to allow forfundus photography and either no evidence of ARM in either eye or havingARM in one eye while the other maintained good vision (20/30 or better)(The Age-Related Eye Disease Study Research Group 1999). DNA samplesfrom the NEI-AREDS Genetic Repository were used for this research.

ARM status was assigned using the AREDS age-related maculopathy gradingsystem and based on phenotypes assigned at the most recent follow-upvisit. Again, only Caucasian individuals are included in the analysis,as the sample size of other groups is too small for reasonable results:there are only 15 African American, 2 Hispanic and 3 individuals ofother races. AREDS cases (n=701) consisted of grade 3, 4 and 5. AREDSsubjects of grade 3 (n=96) have ARM but do not suffer from end-stageARM, subjects of grade 4 (n=266) have end-stage ARM in one eye andsubjects of grade 5 (n=339) have end-stage ARM in both eyes. AREDScontrols (n=175) have AREDS grade 1 (grade 2 individuals were excludedprior to analyses).

Genotyping

The M299V variant in ELOVL4 (rs3812153), the Y402H variant in CFH (rs1061170) and the S69A variant in LOC387715 (rs10490924) were genotypedusing RFLP techniques. The primers, annealing temperatures andrestriction endonuclease for each assay were:5′-AGATGCCGATGTTGTTAAAAG-3′ (F, SEQ ID NO: 13),5′-CATCTGGGTATGGTATTAAC-3′ (R, SEQ ID NO: 14), 50° C. and BspHI forELOVL4; 5′-TCTTTTTGTGCAAACCTTTGTTAG-3′ (F, SEQ ID NO: 15),5′-CCATTGGTAAAACAAGGTGACA-3′ (R, SEQ ID NO: 16), 52° C. and NlaIII forCFH; 5′-GCACCTTTGTCACCACATTA-3′ (F, SEQ ID NO: 17),5′-GCCTGATCATCTGCATTTCT-3′ (R, SEQ ID NO: 18), 54° C. and PvuII forLOC387715.

The A320T variant in PLEKHA1 (rs1045216) was genotyped using 5′exonuclease Assay-on-Demand TaqMan assays (Applied BiosystemsIncorporated). Amplification and genotype assignments were conductedusing the ABI7000 and SDS 2.0 software (Applied BiosystemsIncorporated). For all genotyping conducted for this research,double-masked genotyping assignments were made for each variant,compared and each discrepancy addressed using raw data or byre-genotyping.

Association Analyses

SNP-disease association was measured with allele- and genotypechi-squared tests, and P-values were simulated using 100,000 replicates;in cases with one or more expected cell numbers less then five, theFisher's exact test was used. The strength of the association wasestimated by crude odds ratios (OR) and population attributable risks(PAR). A general formula was used to calculate the PAR:PAR=P_(r)(OR−1)/(1+P_(r)(OR−1)), where P₁ is the prevalence of the riskfactor in the general population. Estimates of P_(r) were derived fromthe CHS controls; this is reasonable, because the CHS subjects were notselected on the basis of ARM disease status, and the number of CHScontrols is large (n=1,051). For comparison purposes, odds ratiosadjusted (OR_(adj)) for age and gender were estimated. Logisticregression models were used to calculate both crude and adjusted oddsratios, using R (38). The less frequent allele in the control group wasconsidered the risk allele, and the OR and OR_(adj) were calculated bycomparing those homozygote for the risk allele (RR) to the baselinegroup (those homozygote for the normal allele [NN]) and comparing thoseheterozygote for the risk allele (RN) to the baseline group. Thecontrasts for dominance (RR and RN versus NN) and recessive (RR versusRN and NN) effects were also evaluated.

Distinguishing Between PLEKHA1 and LOC387715

We employed the haplotype method (Valdes, A. M. and Thomson, G. (1997)Detecting disease-predisposing variants: the haplotype method. Am J HumGenet. 60, 703-716) to identify which one of the two loci, A320T inPLEKHA1 or S69A in LOC387715, is more likely the actual diseasepredisposing variant in the 10q26 region. The basis of the haplotypemethod is simple and elegant (for a mathematical proof, see Valdes andThomson (1997)). If all predisposing variants are included on ahaplotype, then the neutral variants are expected to be in the sameratio in cases and controls on a particular disease-predisposinghaplotype, although the actual frequencies may differ. On the otherhand, if not all predisposing variants have been identified, equality inthe ratios of haplotype frequencies of non-predisposing variants is notexpected.

The expected ratios for the A320T-S69A haplotypes are formulated below,assuming one variant is ARM-predisposing and the other is a neutralvariant. We assume that A320T and S69A are all the ARM-predisposingvariants in the PLEKHA1-LOC387715 haplotype block on chromosome 10q26.Four possible A320T-S69A haplotypes exist: G-G, A-G, G-T, and A-T. IfA320T is the causal locus and S69A the neutral locus, we expect:

$\begin{matrix}{\left\lbrack \frac{f\left( {G - G} \right)}{f\left( {G - T} \right)} \right\rbrack_{controls} = \left\lbrack \frac{f\left( {G - G} \right)}{f\left( {G - T} \right)} \right\rbrack_{cases}} & \left( {1a} \right) \\{\left\lbrack \frac{f\left( {A - G} \right)}{f\left( {A - T} \right)} \right\rbrack_{controls} = \left\lbrack \frac{f\left( {A - G} \right)}{f\left( {A - T} \right)} \right\rbrack_{cases}} & \left( {1b} \right)\end{matrix}$

but, if S69A is the causal locus and A320T the neutral locus, we expect:

$\begin{matrix}{\left\lbrack \frac{f\left( {G - G} \right)}{f\left( {A - T} \right)} \right\rbrack_{controls} = \left\lbrack \frac{f\left( {G - G} \right)}{f\left( {A - G} \right)} \right\rbrack_{cases}} & \left( {2a} \right) \\{\left\lbrack \frac{f\left( {G - T} \right)}{f\left( {A - T} \right)} \right\rbrack_{controls} = \left\lbrack \frac{f\left( {G - T} \right)}{f\left( {A - T} \right)} \right\rbrack_{cases}} & \left( {2b} \right)\end{matrix}$

where f denotes frequencies of a particular haplotype in controls orcases.

The hypotheses of interest are:

-   -   H₀ _(P) : The A329T variant in PLEKHA1 fully accounts for the        ARM predisposition to the PLEKHA1-LOC387715 haplotype block.    -   H₀ _(L) : The S69A variant in LOC387715 fully accounts for the        ARM predisposition to the PLEKHA1-LOC387715 haplotype block.

Rejecting either of these hypotheses means that the tested variant isnot sufficient to account for the ARM predisposition to thePLEKHA1-LOC387715 haplotype block, alone. Four 2×2 contingency tablescan be derived from Formulas 1a, 1b, 2a, and 2b:

TABLE 1a Unexposed Exposed Controls f(G-G) f(G-T) Cases f(G-G) f(G-T)

TABLE 1b Unexposed Exposed Controls f(A-G) f(A-T) Cases f(A-G) f(A-T)

TABLE 2a Unexposed Exposed Controls f(G-G) f(A-G) Cases f(G-G) f(A-G)

TABLE 2b Unexposed Exposed Controls f(G-T) f(A-T) Cases f(G-T) f(A-T)

Under H₀ _(p) we expect homogeneity in contingency tables 1a and 1b, andunder H₀ _(L) we expect homogeneity in contingency tables 2c and 2d.Regular chi-squared statistic may be calculated from each contingencytable to generate a combined statistic. For H₀ _(L) the statistic is themaximum chi-squared from Formulas 1a and 1b, and for H₀ _(L) thestatistic is the maximum chi-squared from Formulas 2a and 2b. However,due to dependency of the statistics derived from each set of contingencytables, the distribution of the combined statistics is not clear. Thelack of independence arises from (1) combining measurementscorresponding to various alleles at predisposing loci, and (2) linkagedisequilibrium between predisposing and non-predisposing loci. Both ofthese conditions are inevitable, (1) because variant always has morethan one allele, and (2) because, if the variants are in completelinkage equilibrium, there is no need to distinguish between theirindependent association signals.

As a result of the dependency in the data a permutation testing needs tobe done conditionally on the allele at the predisposing locus (under thenull hypotheses). We start by grouping the haplotypes (two for eachperson) according to the allele at the predisposing locus. Then thecase-control labels are permuted within each group and a combinedstatistic is calculated for each pair of replicate. This permutationprocedure is similar to the procedure proposed by Li H. (2001) (Apermutation procedure for the haplotype method for identification ofdisease-predisposing variants. Ann Hum Genet. 65:189-196). Phasedgenotype data were not available and haplotypes had to be imputed fromthe unphased genotypes. Haplotype frequencies were estimated separatelyin controls and cases. The program SNPHAP (39) was used to estimate thehaplotype frequencies and phased haplotypes at each subject. SNPHAP usesthe EM algorithm to calculate a maximum likelihood estimate of haplotypefrequencies given the unphased genotype data. The posteriorprobabilities of individual haplotype assignments exceed 94% for everyindividual typed at both A320T and S69A. The estimated haplotypefrequencies are given in Table 11.

TABLE 11 Haplotype frequencies of A320T in PLEKHA1 and S69A in LOC387715(estimated with the program SNPHAP). A320T-S69A AREDS CHS haplotypeControls Cases Controls Cases G-G 0.3928 0.2802 0.3909 0.3188 G-T 0.17900.4149 0.1924 0.3294 A-G 0.4186 0.2792 0.3894 0.3337 A-T 0.0096 0.02570.0272 0.0180 Estimates derived from both the AREDS and CHS cohorts isgiven.

Interaction Analyses

The analyses of interaction were threefold: first, we tested forinteracting genetic effects of Y402H in CFH and S69A in LOC387715 inboth CHS and AREDS samples, then we tested for interaction of both Y402Hand S69A with smoking history in both CHS and AREDS samples, and finallywe calculated joint ORs of the three risk factors.

We followed a modeling strategy proposed by North et al. (North, B. V.,Curtis, D. and Sham, P. C. (2005) Application of logistic regression tocase-control association studies involving two causative loci. HumHered. 59, 79-87). Series of logistic regression models are fitted tothe AREDS and CHS data sets in order to find the model that bestdescribes the joint effects of CFH and LOC387715. For each genotype,models allowing for additive effects (ADD1, ADD2 and ADD-BOTH), andmodels which incorporate dominance effects (DOM1, DOM2 and DOM-BOTH) arefitted. The ADD1 model includes only the term x₁ for additive effects ofCFH, coded as −1 for genotype TT at Y402H, as 0 for genotype CT, and as1 for genotype CC. The ADD2 includes only model term x₂ for additiveeffects of LOC387715, coded as −1 for genotype GG at S69A, as 0 forgenotype GT, and as 1 for genotype TT. The ADD-BOTH models the jointadditive effects of CFH and LOC387715. The DOM1 incorporates dominanceeffects to ADD1, and includes x₁ and z₁, coded as 0.5 for genotype CTand −0.5 for genotypes TT and CC at Y402H. The DOM2 model similarlyincorporates dominance effects to ADD2, and includes x₂ and z₂, coded as0.5 for genotype GT and −0.5 for genotypes GG and TT at S69A. DOM-BOTHmodels the joint dominance effects of CFH and LOC387715. Three furthermodels, that model the interaction between CFH and LOC387715, arefitted: ADD-INT includes the product term x₁*x₂, ADD-DOM includes x₁*x₂,x₁*z₂, and z₁*x₂, and DOM-INT includes x₁*x₂, x₁*z₂, z₁*x₂, and z₁*z₂.

The above modeling strategy was modified to investigate the jointeffects of CFH and smoking, and the joint effects of LOC387715 andsmoking. The modified approach is the same as used by Schmidt et al.(2006) to test for interaction between LOC387715 and smoking. The codingscheme is the same, as above, except that smoking is coded as 0 fornever smokers and 1 for ever smokers. The models fitted for the effectsof CFH and smoking are: ADD 1, SMOKE, ADD1-SMOKE, DOM1, ADD1-SMOKE-INT,and DOM1-SMOKE-INT, and the models fitted for the effects of LOC387715and smoking are: ADD2, SMOKE, ADD2-SMOKE, DOM2, ADD2-SMOKE-INT, andDOM2-SMOKE-INT.

All models were compared by the Akaike's information criterion (AIC).Models for which the AIC differed by <2 are considered indistinguishable(North, B. V., Curtis, D. and Sham, P. C. (2005) Application of logisticregression to case-control association studies involving two causativeloci. Hum Hered. 59, 79-87), and the model with fewer parameters waschosen as the most parsimonious model. Since adjusting for age andgender did not affect the estimates of ORs for Y2402H nor S69A (Tables12 and 13), and to keep number of parameters as small as possible, noadjustment was made for these covariates when modeling interaction.Based on the results of the above interaction analyses, joint ORs werecalculated.

TABLE 12 Estimated crude ORs, corresponding 95% CIs, and PARs,unadjusted for age and gender Dominant Recessive HeterozygotesHomozygotes (RR + RN vs. NN) (RR vs. RN + NN) (RN vs. NN) (RR vs. NN) OR95% CI PAR OR 95% CI PAR OR 95% CI PAR OR 95% CI PAR CFH (Y402H) 1 vs.345 3.73 2.60 5.34 0.60 3.69 2.37 5.75 0.22 2.66 1.81 3.92 0.43 6.694.08 10.98 0.37 1 vs. 45 3.94 2.72 5.71 0.62 3.74 2.39 5.85 0.22 2.821.89 4.19 0.45 7.06 4.27 11.70 0.38 1 vs. 3 2.73 1.55 4.83 0.49 3.391.89 6.10 0.20 1.93 1.04 3.60 0.30 4.95 2.46 9.95 0.29 1 vs. 4 3.64 2.355.64 0.59 3.48 2.14 5.66 0.20 2.67 1.67 4.27 0.43 6.33 3.60 11.16 0.35 1vs. 5 4.21 2.76 6.42 0.64 3.95 2.47 6.32 0.23 2.94 1.87 4.63 0.47 7.714.46 13.34 0.41 1 vs. 45 (GA) 3.73 2.21 6.31 0.60 4.01 2.36 6.82 0.242.54 1.44 4.48 0.41 7.04 3.69 13.41 0.38 1 vs. 4 (GA) 2.71 1.36 5.370.49 4.16 2.14 8.07 0.24 1.68 0.78 3.61 0.23 5.55 2.48 12.41 0.32 1 vs.5 (GA) 4.85 2.46 9.56 0.68 3.91 2.16 7.10 0.23 3.47 1.69 7.14 0.53 8.653.92 19.09 0.44 1 vs. 45 (NV) 3.31 2.16 5.07 0.56 3.24 2.00 5.26 0.192.48 1.57 3.93 0.40 5.60 3.21 9.78 0.32 1 vs. 4 (NV) 3.43 2.05 5.74 0.572.80 1.63 4.80 0.16 2.78 1.61 4.80 0.44 5.24 2.74 10.01 0.30 1 vs. 5(NV) 3.18 1.87 5.41 0.55 3.82 2.21 6.59 0.22 2.17 1.22 3.86 0.34 6.003.12 11.53 0.34 CHS 2.26 1.45 3.53 0.41 2.99 1.85 4.83 0.17 1.82 1.132.92 0.27 4.22 2.39 7.42 0.25 ELOVL4 1 vs. 345 0.69 0.47 1.01 −0.07 0.780.28 2.16 −0.01 0.69 0.46 1.02 −0.06 0.72 0.26 1.99 −0.01 1 vs. 45 0.710.48 1.04 −0.06 0.85 0.30 2.38 0.00 0.70 0.46 1.04 −0.06 0.78 0.28 2.19−0.01 1 vs. 3 0.59 0.32 1.09 −0.09 0.35 0.04 3.04 −0.02 0.62 0.33 1.17−0.07 0.31 0.04 2.75 −0.02 1 vs. 4 0.67 0.43 1.06 −0.07 0.76 0.23 2.53−0.01 0.67 0.42 1.07 −0.06 0.69 0.21 2.32 −0.01 1 vs. 5 0.73 0.48 1.12−0.06 0.92 0.30 2.80 0.00 0.72 0.46 1.12 −0.05 0.85 0.28 2.60 0.00 1 vs.45 (GA) 0.59 0.35 1.00 −0.09 0.91 0.24 3.47 0.00 0.56 0.32 0.99 −0.080.81 0.21 3.08 0.00 1 vs. 4 (GA) 0.63 0.31 1.29 −0.08 1.13 0.21 5.990.00 0.58 0.27 1.26 −0.08 1.00 0.19 5.36 0.00 1 vs. 5 (GA) 0.56 0.301.06 −0.10 0.77 0.15 4.04 −0.01 0.55 0.28 1.07 −0.09 0.67 0.13 3.57−0.01 1 vs. 45 (NV) 0.65 0.42 1.01 −0.07 0.36 0.09 1.55 −0.02 0.69 0.431.09 −0.06 0.33 0.08 1.42 −0.02 1 vs. 4 (NV) 0.71 0.42 1.18 −0.06 0.670.16 2.84 −0.01 0.72 0.42 1.22 −0.05 0.62 0.14 2.64 −0.01 1 vs. 5 (NV)0.58 0.33 1.03 −0.09 — — — — 0.65 0.37 1.15 −0.06 — — — — CHS 1.41 0.922.17 0.07 0.33 0.04 2.43 −0.02 1.55 1.00 2.41 0.09 0.36 0.05 2.68 −0.02PLEKHA1 (A320T) 1 vs. 345 0.57 0.40 0.81 −0.39 0.37 0.23 0.60 −0.13 0.680.47 0.99 −0.18 0.31 0.18 0.51 −0.14 1 vs. 45 0.56 0.39 0.81 −0.40 0.390.24 0.63 −0.12 0.67 0.46 0.98 −0.19 0.31 0.19 0.53 −0.14 1 vs. 3 0.620.37 1.05 −0.33 0.28 0.11 0.70 −0.15 0.78 0.46 1.35 −0.12 0.25 0.09 0.64−0.16 1 vs. 4 0.68 0.46 1.02 −0.26 0.62 0.37 1.04 −0.07 0.75 0.49 1.15−0.13 0.53 0.30 0.94 −0.09 1 vs. 5 0.48 0.33 0.71 −0.51 0.22 0.12 0.42−0.16 0.61 0.41 0.92 −0.23 0.17 0.09 0.34 −0.17 1 vs. 45 (GA) 0.70 0.441.12 −0.24 0.42 0.21 0.83 −0.12 0.84 0.52 1.37 −0.08 0.38 0.18 0.80−0.12 1 vs. 4 (GA) 0.66 0.36 1.21 −0.29 0.57 0.24 1.38 −0.08 0.73 0.381.40 −0.15 0.48 0.19 1.24 −0.10 1 vs. 5 (GA) 0.74 0.43 1.27 −0.20 0.320.13 0.79 −0.14 0.92 0.53 1.63 −0.04 0.30 0.12 0.80 −0.14 1 vs. 45 (NV)0.50 0.33 0.74 −0.49 0.45 0.26 0.78 −0.11 0.57 0.37 0.87 −0.26 0.34 0.190.61 −0.13 1 vs. 4 (NV) 0.65 0.41 1.02 −0.30 0.68 0.37 1.24 −0.06 0.690.42 1.12 −0.18 0.56 0.29 1.07 −0.09 1 vs. 5 (NV) 0.37 0.23 0.59 −0.710.21 0.08 0.51 −0.16 0.46 0.28 0.76 −0.35 0.14 0.06 0.36 −0.18 CHS 0.760.52 1.11 −0.19 0.68 0.39 1.18 −0.06 0.81 0.54 1.21 −0.10 0.61 0.34 1.10−0.07 LOC387715 (S69A) 1 vs. 345 3.99 2.81 5.67 0.54 10.16 3.70 27.880.28 3.06 2.13 4.39 0.42 17.26 6.22 47.89 0.41 1 vs. 45 4.17 2.92 5.960.56 10.52 3.83 28.93 0.29 3.18 2.20 4.60 0.43 18.30 6.57 50.93 0.43 1vs. 3 3.07 1.82 5.17 0.45 7.97 2.56 24.81 0.23 2.45 1.42 4.23 0.34 11.893.70 38.19 0.32 1 vs. 4 2.72 1.83 4.05 0.41 5.64 1.95 16.27 0.17 2.341.55 3.53 0.32 8.19 2.80 24.00 0.24 1 vs. 5 6.14 4.11 9.19 0.67 15.075.43 41.82 0.38 4.32 2.85 6.57 0.54 32.07 11.30 91.01 0.57 1 vs. 45 (GA)3.29 2.07 5.21 0.48 6.28 2.09 18.93 0.19 2.81 1.74 4.52 0.39 10.14 3.2831.31 0.28 1 vs. 4 (GA) 3.06 1.66 5.65 0.45 4.75 1.29 17.49 0.14 2.741.46 5.17 0.38 7.57 1.97 29.06 0.22 1 vs. 5 (GA) 3.46 2.01 5.94 0.497.38 2.33 23.38 0.22 2.86 1.63 5.02 0.40 12.02 3.65 39.57 0.32 1 vs. 45(NV) 4.09 2.73 6.12 0.55 8.62 3.05 24.39 0.25 3.30 2.17 5.01 0.45 15.345.32 44.25 0.38 1 vs. 4 (NV) 2.71 1.72 4.27 0.40 4.42 1.42 13.74 0.132.44 1.53 3.90 0.34 6.58 2.07 20.90 0.19 1 vs. 5 (NV) 7.21 4.24 12.270.71 14.44 4.96 41.99 0.37 5.24 3.02 9.10 0.60 35.22 11.47 108.17 0.60CHS 1.93 1.32 2.83 0.27 3.91 2.17 7.03 0.11 1.58 1.05 2.39 0.17 4.752.56 8.80 0.14 NOTE N denotes the normal allele and R denotes the riskallele. The risk allele is defined as the least frequent allele incontrols. The OR for dominance effects compares those who carry one riskallele (RN and RR genotypes) to individuals homozygote for the normalallele (NN), the OR for recessice effects compares individuals with RRgenotype to those who carry one normal allele (NN and RN genotypes).Hetero- and homozygote ORs compare individuals with one (RN) and two(RR) risk alleles to individuals with NN genotype, respectively. GA =geographic atrophy. CNV = choroidal neovascular membranes.

TABLE 13 Estimated ORs, corresponding 95% CIs, and PARs, adjusted forage and gender Dominant Recessive Heterozygotes Homozygotes (RR + RN vs.NN) (RR vs. RN + NN) (RN vs. NN) (RR vs. NN) OR 95% CI PAR OR 95% CI PAROR 95% CI PAR OR 95% CI PAR CFH (Y402H) 1 vs. 345 3.52 2.43 5.10 0.583.78 2.40 5.95 0.22 2.47 1.66 3.68 0.40 6.43 3.88 10.64 0.36 1 vs. 453.69 2.51 5.41 0.60 3.79 2.40 6.00 0.22 2.60 1.72 3.93 0.42 6.71 4.0111.23 0.37 1 vs. 3 2.73 1.52 4.91 0.49 3.73 2.01 6.91 0.22 1.88 0.993.59 0.28 5.23 2.53 10.83 0.30 1 vs. 4 3.41 2.18 5.32 0.57 3.73 2.266.14 0.22 2.41 1.48 3.90 0.39 6.20 3.50 10.97 0.35 1 vs. 5 4.17 2.666.53 0.64 3.85 2.35 6.32 0.23 2.95 1.83 4.78 0.47 7.69 4.25 13.90 0.41 1vs. 45 (GA) 3.54 2.07 6.07 0.58 3.87 2.24 6.69 0.23 2.44 1.37 4.36 0.396.60 3.43 12.71 0.36 1 vs. 4 (GA) 2.62 1.31 5.24 0.47 4.12 2.10 8.110.24 1.63 0.75 3.54 0.22 5.46 2.43 12.25 0.31 1 vs. 5 (GA) 4.86 2.419.82 0.68 3.71 1.98 6.94 0.22 3.55 1.69 7.47 0.53 8.51 3.68 19.68 0.43 1vs. 45 (NV) 3.16 2.02 4.95 0.54 3.42 2.06 5.67 0.20 2.33 1.44 3.76 0.375.47 3.07 9.75 0.31 1 vs. 4 (NV) 3.30 1.95 5.59 0.56 3.11 1.77 5.44 0.182.57 1.46 4.53 0.41 5.27 2.73 10.16 0.30 1 vs. 5 (NV) 3.10 1.74 5.520.54 3.90 2.14 7.10 0.23 2.11 1.13 3.94 0.33 6.04 2.93 12.49 0.34 CHS2.10 1.34 3.31 0.38 3.12 1.90 5.15 0.18 1.65 1.02 2.67 0.23 4.19 2.347.52 0.25 ELOVL4 1 vs. 345 0.68 0.46 1.01 −0.07 0.68 0.24 1.95 −0.010.69 0.46 1.04 −0.06 0.64 0.22 1.83 −0.01 1 vs. 45 0.70 0.47 1.05 −0.060.76 0.26 2.19 −0.01 0.70 0.46 1.07 −0.05 0.71 0.24 2.05 −0.01 1 vs. 30.62 0.33 1.17 −0.08 0.31 0.03 2.76 −0.02 0.66 0.35 1.27 −0.06 0.28 0.032.56 −0.02 1 vs. 4 0.68 0.43 1.07 −0.07 0.72 0.21 2.48 −0.01 0.68 0.421.10 −0.06 0.68 0.20 2.33 −0.01 1 vs. 5 0.73 0.46 1.16 −0.06 0.76 0.242.46 −0.01 0.74 0.46 1.19 −0.05 0.71 0.22 2.29 −0.01 1 vs. 45 (GA) 0.590.34 1.03 −0.09 0.68 0.17 2.70 −0.01 0.59 0.33 1.06 −0.08 0.61 0.15 2.45−0.01 1 vs. 4 (GA) 0.64 0.31 1.33 −0.08 0.92 0.17 5.05 0.00 0.61 0.281.33 −0.07 0.84 0.15 4.61 0.00 1 vs. 5 (GA) 0.55 0.28 1.08 −0.10 0.570.10 3.23 −0.01 0.56 0.28 1.14 −0.08 0.52 0.09 2.93 −0.01 1 vs. 45 (NV)0.68 0.43 1.08 −0.07 0.37 0.08 1.68 −0.02 0.72 0.44 1.16 −0.05 0.35 0.081.57 −0.02 1 vs. 4 (NV) 0.68 0.40 1.16 −0.07 0.66 0.15 2.91 −0.01 0.690.40 1.20 −0.06 0.61 0.14 2.69 −0.01 1 vs. 5 (NV) 0.71 0.38 1.30 −0.06 —— — — 0.80 0.43 1.50 −0.04 — — — — CHS 1.35 0.87 2.11 0.07 0.27 0.032.15 −0.02 1.51 0.96 2.37 0.08 0.29 0.04 2.33 −0.02 PLEKHA1 (A320T) 1vs. 345 0.61 0.43 0.88 −0.34 0.40 0.25 0.65 −0.12 0.73 0.50 1.08 −0.150.34 0.20 0.57 −0.13 1 vs. 45 0.61 0.42 0.88 −0.35 0.41 0.25 0.68 −0.120.72 0.48 1.06 −0.16 0.35 0.20 0.59 −0.13 1 vs. 3 0.70 0.41 1.19 −0.250.30 0.12 0.76 −0.14 0.87 0.50 1.52 −0.07 0.27 0.10 0.72 −0.15 1 vs. 40.71 0.47 1.08 −0.23 0.69 0.40 1.18 −0.06 0.76 0.49 1.18 −0.13 0.57 0.321.02 −0.08 1 vs. 5 0.55 0.36 0.82 −0.42 0.19 0.10 0.38 −0.17 0.71 0.461.09 −0.16 0.16 0.08 0.33 −0.18 1 vs. 45 (GA) 0.75 0.47 1.21 −0.19 0.440.22 0.88 −0.11 0.90 0.54 1.48 −0.05 0.41 0.19 0.88 −0.12 1 vs. 4 (GA)0.68 0.37 1.27 −0.26 0.59 0.24 1.44 −0.08 0.77 0.40 1.50 −0.12 0.47 0.181.23 −0.10 1 vs. 5 (GA) 0.84 0.47 1.48 −0.12 0.31 0.12 0.80 −0.14 1.040.58 1.89 0.02 0.32 0.12 0.89 −0.14 1 vs. 45 (NV) 0.55 0.36 0.83 −0.420.48 0.27 0.86 −0.10 0.62 0.40 0.97 −0.22 0.38 0.20 0.70 −0.12 1 vs. 4(NV) 0.68 0.42 1.08 −0.27 0.75 0.41 1.39 −0.05 0.70 0.42 1.16 −0.17 0.600.31 1.16 −0.08 1 vs. 5 (NV) 0.46 0.27 0.76 −0.55 0.18 0.07 0.48 −0.170.59 0.35 1.02 −0.24 0.14 0.05 0.39 −0.18 CHS 0.72 0.49 1.07 −0.22 0.680.39 1.19 −0.06 0.77 0.51 1.17 −0.12 0.58 0.32 1.07 −0.08 LOC387715(S69A) 1 vs. 345 3.91 2.72 5.62 0.54 11.02 3.97 30.56 0.30 2.94 2.034.27 0.41 19.51 6.91 55.09 0.44 1 vs. 45 4.03 2.78 5.83 0.55 11.52 4.1332.08 0.31 3.00 2.05 4.39 0.41 21.25 7.46 60.54 0.47 1 vs. 3 3.19 1.855.49 0.46 8.35 2.55 27.38 0.24 2.59 1.46 4.60 0.36 13.61 3.86 47.97 0.351 vs. 4 2.67 1.78 4.02 0.40 6.29 2.13 18.52 0.19 2.26 1.48 3.45 0.319.50 3.14 28.69 0.27 1 vs. 5 5.88 3.83 9.03 0.66 18.13 6.31 52.08 0.434.00 2.56 6.25 0.51 44.22 14.47 135.13 0.65 1 vs. 45 (GA) 3.05 1.90 4.890.45 7.41 2.35 23.39 0.22 2.53 1.55 4.14 0.35 12.30 3.76 40.22 0.33 1vs. 4 (GA) 2.91 1.56 5.41 0.43 6.12 1.56 24.05 0.18 2.55 1.34 4.84 0.3511.81 2.71 51.45 0.32 1 vs. 5 (GA) 3.14 1.79 5.51 0.46 8.83 2.56 30.430.25 2.52 1.40 4.54 0.35 14.26 3.91 52.00 0.36 1 vs. 45 (NV) 3.90 2.565.94 0.53 8.85 3.05 25.70 0.25 3.13 2.03 4.85 0.43 16.84 5.58 50.84 0.411 vs. 4 (NV) 2.64 1.65 4.21 0.39 5.13 1.59 16.54 0.15 2.34 1.44 3.800.32 7.72 2.30 25.96 0.23 1 vs. 5 (NV) 6.61 3.73 11.72 0.69 14.65 4.6945.82 0.37 4.85 2.67 8.80 0.58 48.87 13.23 180.53 0.67 CHS 1.86 1.262.76 0.25 4.17 2.25 7.74 0.12 1.51 0.99 2.30 0.15 5.10 2.66 9.78 0.15NOTE N denotes the normal allele and R denotes the risk allele. The riskallele is defined as the least frequent allele in controls. The OR fordominance effects compares those who carry one risk allele (RN and RRgenotypes) to individuals homozygote for the normal allele (NN), the ORfor recessice effects compares individuals with RR genotype to those whocarry one normal allele (NN and RN genotypes). Hetero- and homozygoteORs compare individuals with one (RN) and two (RR) risk alleles toindividuals with NN genotype, respectively. GA = geographic atrophy. CNV= choroidal neovascular membranes.

APOE Analyses

Previous studies have reported possible protective and harmful effectsof the apolipoprotein E (APOE) gene in ARM. The ε4 allele may haveprotective effects (Klaver, C. C., et al. (1998) Genetic association ofapolipoprotein E with age-related macular degeneration. Am J Hum Genet.63, 200-206; Schmidt, S., et al. (2000) Association of theapolipoprotein E gene with age-related macular degeneration: possibleeffect modification by family history, age, and gender. Mol Vis. 6,287-293; Schmidt, S., et al. (2002) A pooled case-control study of theapolipoprotein E (APOE) gene in age-related maculopathy. OphthalmicGenet. 23, 209-223; Baird, P. N., et al. (2004) The epsilon2 andepsilon4 alleles of the apolipoprotein gene are associated withage-related macular degeneration. Invest Ophthalmol Vis Sci. 45,1311-1315 and Zareparsi, S., et al. (2004) Association of apolipoproteinE alleles with susceptibility to age-related macular degeneration in alarge cohort from a single center. Invest Ophthalmol Vis Sci. 45,1306-1310), while the least frequent allele, 82, may increase the riskof ARM (Klaver, C. C., et al. (1998) and Zareparsi, S., et al. (2004).The APOE variant was genotype by CHS and its association with ARM wasassessed in this study. Individuals were classified by APOE genotypeinto individuals with APOE-ε3/ε3 genotype, and APOE-22 and APOE-24carriers (denoted APOE-ε2/* and APOE-ε4/*, respectively); individualswith APOE-ε2/ε4 genotype were included in both the APOE-ε2/* andAPOE-ε4/* groups. Chi-squared tests were used to test for differences indistributions of APOE-ε3/ε3 and APOE-2ε*/, and APOE-3ε/3ε and APOE-4ε/*,genotypes in controls and cases.

Meta-Analyses

We undertook a meta-analysis approach to pool estimated OR frompreviously published reports on CFH and LOC387715 and the two reportspresented here. Initially data were analyzed, assuming the between-studyvariation is due to chance, and fixed-effects model was employed. Underthe fixed-effect model, the maximum likelihood estimator of the pooledOR is an average of individual estimates, weighted by the inverse oftheir variances, and the variance of the pooled OR is estimated by theinverse of the sum of individual weights. Meta-analyses underhomogeneity were performed in R (RDevelopmentCoreTeam (2005) R: Alanguage and environment for statistical computing. R Foundation forStatistical Computing, Vienna, Austria). The assumption of homogeneitywas checked using a chi-squared test. However, tests of homogeneity tendto have low power, and therefore, for comparison, we also pooled the ORin a random effects setting. Meta-analyses under heterogeneity wereperformed using the method of restricted maximum likelihood (REML), asimplemented in SAS Proc Mixed (SAS software release 8.2 [SAS InstituteInc., Cary, N.C., USA]). The pooled REML estimator is identical to theDerSimonian-Laird estimator (DerSimonian, R. and Laird, N. (1986)Meta-analysis in clinical trials. Control Clin Trials. 7, 177-188 andvan Houwelingen, H. C., Arends, L. R. and Stijnen, T. (2002) Advancedmethods in meta-analysis: multivariate approach and meta-regression.Stat Med. 21, 589-624). The SAS codes by van Houwelingen et al. 2002were modified to perform the analyses under heterogeneity.

The Y402H variant within CFH has been found strongly associated with ARMin eleven studies (Edwards, A. O., et al. (2005) Complement factor Hpolymorphism and age-related macular degeneration. Science. 308,421-424; Haines, J. L., et al. (2005) Complement factor H variantincreases the risk of age-related macular degeneration. Science. 308,419-421; Klein, R. J., et al. (2005) Complement factor H polymorphism inage-related macular degeneration. Science. 308, 385-389; Hageman, G. S.,et al. (2005) A common haplotype in the complement regulatory genefactor H (HF1/CFH) predisposes individuals to age-related maculardegeneration. Proc Natl Acad Sci USA; Conley, Y. P., et al. (2005)Candidate gene analysis suggests a role for fatty acid biosynthesis andregulation of the complement system in the etiology of age-relatedmaculopathy. Hum Mol Genet. 14, 1991-2002; Zareparsi, S., et al. (2005)Strong association of the Y402H variant in complement factor H at 1q32with susceptibility to age-related macular degeneration. Am J Hum Genet.77, 149-153; Sepp, T., et al. (2006) Complement factor H variant Y402His a major risk determinant for geographic atrophy and choroidalneovascularization in smokers and nonsmokers. Invest Ophthalmol Vis Sci.47, 536-540; Rivera, A., et al. (2005) Hypothetical LOC387715 is asecond major susceptibility gene for age-related macular degeneration,contributing independently of complement factor H to disease risk. HumMol Genet. 14, 3227-3236; Souied, E. H., et al. (2005) Y402H complementfactor H polymorphism associated with exudative age-related maculardegeneration in the French population. Mol Vis. 11, 1135-1140;Magnusson, K. P., et al. (2006) CFH Y402H confers similar risk of softdrusen and both forms of advanced AMD. PLoS Med. 3, e5 and Jakobsdottir,J., et al. (2005) Susceptibility genes for age-related maculopathy onchromosome 10q26. Am J Hum Genet. 77, 389-407); two of these elevenstudies are ours, so only the results from our Jakobsdottir et al.(2005) paper, that evaluated all contrasts, were used in meta-analysis.The Klein et al. (2005) study used a small subset of the AREDS sample,and the Magnusson et al. (2006) paper only reported allele based ORs andno genotype counts. Therefore these two studies were not included.Results from the Haines et al. (2005) study were included in pooledestimates of ORs for hetero- and homozygotes; genotype counts were notavailable to evaluate contrasts for dominance and recessive effects.Three studies have reported highly associated variant, S69A, within thehypothetical LOC387715 (Rivera, A. et al. (2005); Jakobsdottir, et al.(2005); Schmidt et al. (2006) and Schmidt (2006) Cigarette smokingstrongly modifies the association of LOC387715 and age-related maculardegeneration. Am J Hum Genet. in press). All three reports on LOC387715were included in the meta-analysis. Research participants in all studiesof CFH and LOC387715 are non-Hispanic whites of European and EuropeanAmerican descent. Tables 14 and 15 summarize the studies included in themeta-analyses of CFH and LOC387715, respectively.

TABLE 14 Characteristics of studies included in meta-analysis of Y402Hin CFH Frequency Sample Mean age % HWE^(c) of the Study and samplesize^(a) (±SD)^(b) Males P-value C allele Edwards et al.^(d) Discoverysample Controls 131 67.6 (7.6) 42 0.99 0.340 Cases 225  72.7 (10.1) 580.42 0.553 Replication sample Controls 59 68.1 (9.0) 35 0.28 0.390 Cases170 78.2 (7.9) 65 0.64 0.544 Haines et al.^(e) Controls 185 ≧55 — — —Cases 495 ≧55 — — — Zareparsi et al. Controls 275 ≧68 — 0.11 0.338 Cases616 — — 0.15 0.608 Hageman et al.^(f) Columbia sample Controls 272 68.8(8.6) — 0.23 0.344 Cases 549 71.3 (8.9) — 0.86 0.538 Iowa sampleControls 131 78.4 (7.4) — 0.70 0.336 Cases 403 79.5 (7.8) — 0.22 0.589Jakobsdottir et al. Controls 108 72.6 (8.9) 47 0.26 0.310 Cases 434 68.9(8.8) 39 0.42 0.613 Rivera et al.^(g) Original sample Controls 611 76.2(5.3) 38 <0.01 0.382 Cases 793 76.3 (6.9) 36 0.30 0.595 Replicationsample Controls 335 68.3 (8.1) 45 0.48 0.358 Cases 373 75.0 (7.5) 350.13 0.617 Souied et al. Controls 91 74.6 (6.3) 42 0.21 0.302 Cases 14174.3 (8.0) 38 0.30 0.564 Sepp et al. Controls 262 75.8 (7.8) 42 0.140.363 Cases 443 80.3 (6.9) 45 0.49 0.607 AREDS (1 vs. 345) Controls 17376.5 (4.4) 49 0.25 0.358 Cases 699 79.5 (5.2) 42 0.03 0.612 CHS Controls907 70.3 (3.9) 43 0.55 0.327 Cases 110 73.2 (4.8) 44 0.71 0.495^(a)Sample sizes based on total number of genotyped persons whengenotype counts are available other wise on total sample size, notaccounting for missing data. ^(b)Mean age and corresponding standarddeviation, or other summary statistic available from the orginal paper.^(c)When genotype counts are avaible P-value, derived from the exct test(implemented in R Genetics package), given. ^(d)The two data sets ofEdwards et al. paper are combined in the meta-analysis. HWE P-values forthe combined controls and cases are 0.53 and 0.36, respectively.^(e)Results form Haines et al. paper are included in meta-analysis ofORs for hetero- and homozygote individuals. Sample sizes are based ontotal number of individuals, not accounting for missing genotype data atY402H in CFH. ^(f)The two data sets of Hageman et al. paper are notcombined, following the orignial paper. ^(g)The two data sets of Riveraet al. paper are combined in the meta-analysis. HWE P-values for thecombined controls and cases are 0.03 and 0.09, respectively.

TABLE 15 Characteristics of studies included in meta-analysis of S69A inLOC387715 Sample Mean age % HWE^(c) Frequency of Study and samplesize^(a) (±SD)^(b) Males P-value the T allele Jakobsdottir et al.Controls 106 72.6 (8.9) 47 0.21 0.193 Cases 456 68.9 (8.8) 39 0.06 0.485Rivera et al.^(d) Original sample Controls 594 76.2 (5.3) 38 0.30 0.196Cases 759 76.3 (6.9) 36 0.14 0.417 Replication sample Controls 328 68.3(8.1) 45 0.75 0.215 Cases 361 75.0 (7.5) 35 0.01 0.460 Schmidt etal.^(e) Controls 186 66.7 (8.1) 43 0.55 0.247 Cases 758 76.8 (7.7) 35<0.01 0.427 AREDS (1 vs. 345) Controls 172 76.5 (4.4) 49 0.45 0.189Cases 693 79.5 (5.2) 42 0.99 0.441 CHS Controls 995 70.3 (3.9) 43 0.410.220 Cases 120 73.2 (4.8) 44 0.24 0.354 ^(a)Sample sizes based on totalnumber of genotyped persons when genotype counts are available otherwise on total sample size, not accounting for missing data. ^(b)Mean ageand corresponding standard deviation, or other summary statisticavailable from the orginal paper. ^(c)When genotype counts are avaibleP-value, derived from the exct test (implemented in R Genetics package),given. ^(d)The two data sets of Rivera et al. paper are combined in themeta-analysis. HWE P-values for the combined controls and cases are 0.31and 0.01, respectively. ^(e)In the meta-analysis only grade 1 subjectsare classified as controls (grade 2 subjects are dropped). The originalstudy by Schmidt et al. classified grade 2 individudals as controls. Themean age and % males of controls is taken from the paper and based onboth grade 1 and 2

Results

To further evaluate CFH, ELOVL4, PLEKHA1, and LOC387715 in ARM, wegenotyped previously reported SNPs within all four genes in samples fromthe AREDS and CHS studies. Separate analyses were performed on each dataset, using total of 701 non-Hispanic white ARM patients and 175 controlsfrom the AREDS study, and total of 126 non-Hispanic white ARM patientsand 1051 controls from the CHS study (see, Table 10 for sample sizes andother characteristics of the data, and Table 16 for genotypefrequencies). The disease status of subjects at their last follow-upvisit was the primary endpoint evaluated for AREDS subjects. The AREDSsubjects include controls of grade 1 and cases (grades 3-5) withmoderate ARM and advanced ARM in one or both eyes. The ARM diseasestatus of CHS subjects was evaluated by a single expert, forconsistency, using monocular, nonmydriatic fundus photographs taken atthe 8-year follow-up visit. The majority of CHS cases had moderate ARMincluding multiple drusen with and without pigment epithelial changes(equivalent to AREDS grade 3) with a small number of cases havinggeographic atrophy (GA) or choroidal neovascular membranes (CNV) and theCHS controls are of AREDS grade 1 with the exclusion of those cases withsignificant extramacular drusen.

TABLE 16 Genotype distribution by ARM status in AREDS and CHS cohorts.Genotype frequencies in Gene AREDS AREDS CHS (Variant) and cases CHScases controls controls HapMap genotypes (n = 701) (n = 126) (n = 175)(n = 1051 (CEU) CFH (Y402H) TT 0.170 0.264 0.434 0.448 — CT 0.435 0.4820.416 0.450 — CC 0.395 0.255 0.150 0.103 — ELOVL4 (M299V) AA 0.781 0.7420.711 0.802 0.717 AG 0.195 0.250 0.259 0.174 0.233 GG 0.024 0.008 0.0300.024 0.050 PLEKHA1 (A320T) GG 0.474 0.411 0.339 0.346 0.317 AG 0.4430.460 0.464 0.476 0.467 AA 0.084 0.129 0.196 0.178 0.217 LOC387715(S69A) GG 0.313 0.442 0.645 0.604 0.583 GT 0.492 0.408 0.331 0.353 0.400TT 0.195 0.150 0.023 0.043 0.017 For comparison estimates from derivedfrom the CEU population (residence of Utah with ancestry from northernand western Europe) of the International HapMap project are shown. AREDScases are of grades 3-5 and AREDS controls of grade 1. Genotype countsare available by each grade and subphenotype in Table 17. Description ofthe HapMap CEU populations is provided herein.

TABLE 17 Genotype distributions in AREDS and CHS cohorts, by ARM StatusAREDS Cases (n = 701) Controls Grade 4 (n = 266) Grade 5 (n = 339) CHSGene (Variant) Grade 1 Grade 2^(a) Grade 3 GA CNV GA CNV Controls Casesand Genotype (n = 175) (n = 63) (n = 96) All only only All only only (n= 1051) (n = 126) CFH (Y402H) TT 75 19 21 46 13 27 52 12 25 406 29 CT 7235 39 118 21 72 147 40 52 408 53 CC 26 9 36 101 25 49 139 36 52 93 28All 173 63 96 265 59 148 338 88 129 907 110 ELOVL4 (M299V) AA 118 55 75204 47 115 249 70 97 826 92 AG 43 7 17 50 10 30 65 14 23 179 31 GG 5 0 16 2 3 9 2 0 25 1 All 166 62 93 260 59 148 323 86 120 1030 124 PLEKHA1(A320T) GG 57 24 42 111 25 65 169 34 73 355 51 AG 78 31 45 114 25 61 14243 46 489 57 AA 33 7 6 34 7 21 17 6 6 183 16 All 168 62 93 259 57 147328 83 125 1027 124 LOC387715 (S69A) GG 111 40 35 105 22 59 77 30 26 60153 GT 57 19 44 126 31 74 171 44 70 351 49 TT 4 3 15 31 6 14 89 13 33 4318 All 172 62 94 262 59 147 337 87 129 995 120 NOTE Genotypes areordered: NN - RN - RR, where N is the normal allele and R is the riskallele. The risk allele is defined as the least frequent allele incontrols. GA = geographic atrophy. CNV = choroidal neovascularmembranes. ^(a)Grade 2 AREDS subjects were not included in the analysis.

Association Analyses

For each gene, CFH, ELOVL4, PLEKHA1 , and LOC387715, association withARM was assessed by a chi-squared statistic. The magnitude of the effectof each gene was estimated by odds ratios (ORs) and populationattributable risks (PARs). To evaluate whether the genes confersimilarly to early and advanced ARM, ORs were calculated for each gradeand subtype (GA and CNV) separately using the AREDS data.

CFH: The association of the Y402H variant in CFH with ARM is significant(P≦0.00001) in both the AREDS and CHS cohorts (Table 18), confirmingearlier findings by ourselves (Conley et al. (2005) and Jakobsdottir etal. (2005)) and others (Edwards et al. (2005; Haines et al. (2006);Klein et al. (2005) and Rivera et al. (2005)). The estimated ORs forY402H in CFH suggest that the variant confers similar risk to all stagesof ARM and both forms of advanced ARM, GA and CNV (FIG. 7 and Table 12).

TABLE 18 Results of allele- and genotype association tests. Gene(Variant) and Comparison CFH ELOVL4 PLEKHA1 LOC387715 in AREDS orP-value for test P-value for test P-value for test P-value for test CHSAllele Genotype Allele Genotype^(a) Allele Genotype Allele GenotypeAREDS 1 vs. 345 ≦0.00001 ≦0.00001 0.06775 0.13963 0.00004 0.00004≦0.00001 ≦0.00001 1 vs. 5 ≦0.00001 ≦0.00001 0.20518 0.32438 ≦0.00001≦0.00001 ≦0.00001 ≦0.00001 1 vs. 5 (GA)^(b) ≦0.00001 ≦0.00001 0.104650.21869 0.04131 0.03862 ≦0.00001 ≦0.00001 1 vs. 5 (CNV)^(c) ≦0.00001≦0.00001 0.03445 0.04851 ≦0.00001 ≦0.00001 ≦0.00001 ≦0.00001 CHS≦0.00001 ≦0.00001 0.33832 0.07819 0.07626 0.22544 ≦0.00001 ≦0.00001P-values <0.05 are bolded. ^(a)2-sided P-values from Fisher's exacttest. ^(b)ARM cases have GA in both eyes. ^(c)ARM cases have CNV in botheyes.

An allele-dose effect appears to be present, with carriers of two Calleles at higher risk of ARM than carriers of one C allele (Table 12and FIG. 8). Despite the increased risk in carriers of two C alleles,the population attributable risk (PAR) is similar for the two riskgenotypes, owing to relatively high frequency of the CT genotypecompared to the CC genotype in the general population. PAR estimatesderived from the CHS dataset suggest that the CT and CC genotypesexplain 27% and 25% of ARM in the non-Hispanic white population,respectively. ELOVL4: The M299V variant in ELOVL4 is significantlyassociated (P=0.034) with exudative ARM in the AREDS sample (Table 18),in agreement with our previous findings (Conley, Y. P., et al. (2005).However, no ORs are statistically significant at 95% significance level(FIGS. 7 and 9 and Table 12). These results do not exclude the potentialrole of ELOVL4 in ARM, but do not strongly support it. The small numberof individuals with exudative ARM did not allow for subphenotypeanalysis in the CHS cohort.

PLEKHA1 and LOC387715: The association of the S69A variant in LOC387715with all presentations of ARM is extremely significant (P≦0.00001) inboth the AREDS and CHS data sets (Table 18), confirming earlier findingsby ourselves (Conley, Y. P., et al. (2005) and Jakobsdottir, J., et al.(2005)) and others (Edwards, A. O., et al. (2005); Haines, J. L., et al.(2005); Klein, R. J., et al. (2005); Rivera, A., et al. (2005) andSchmidt, S., et al. (2006)). The A320T variant in PLEKHA1, which islocated on the same haplotype block as LOC387715, is highly significant(P=0.00004) in the AREDS sample but only borderline significant (P=0.08)in the CHS sample. The degree of linkage disequilibrium between A320Tand S69A is statistically significant in both AREDS (D′=0.66) and CHS(D′=0.65) controls. In order to identify which gene, PLEKHA1 orLOC387715, more likely harbors the true ARM-predisposing variant, weapplied the haplotype method (Valdes, A. M. and Thomson, G. (1997)Detecting disease-predisposing variants: the haplotype method. Am J HumGenet. 60, 703-716). According to the haplotype method, the relativefrequency of alleles at neutral variants is expected to be the same incases and controls for a haplotype containing all the predisposingvariants. The results based on applying the method suggest that S69A inLOC387715, and not A320T in PLEKHA1, is an ARM-predisposing variant (see“Distinguishing between PLEKHA1 and LOC387715,” herein). Further, bypermutation testing of the null hypothesis: H₀: the S69A variant inLOC387715 fully accounts for the ARM predisposition to thePLEKHA1-LOC387715 haplotype block, is not rejected (P=0.92 in the AREDSdata, P=0.45 in the CHS data), while a similar hypothesis for A320T isrejected (P≦0.0001 in the AREDS data, P=0.0002 in the CHS data).

The S69A variant in LOC387715 shows different risk patterns than Y402Hin CFH. The variant appears to increase the risk of severe ARMsubstantially more than the risk of mild ARM (FIGS. 7 and 10 [FIG. 11gives complete results for PLEKHA1] and Table 12) in the AREDS datawhere severity of disease is differentiated. For example, the OR forAREDS cases of grade 3, who carry one or two T alleles, is 3.07 (95% CI1.82-5.17), while the OR for AREDS cases, with CNV in both eyes, whocarry one or two T alleles, is 7.21 (95% CI 4.24-12.27). Similar to CFH,S69A shows an allele-dose effect without dramatic differences in thepopulation attributable risk of the GT and TT genotypes (Table 12 andFIG. 10). Since only four AREDS controls are TT homozygous at S69A,point estimates and confidence intervals, for recessive and homozygotecontrasts, derived from regular logistic regression were compared toestimates from exact regression (models fitted in SAS software release8.2 [SAS Institute Inc., Cary, N.C., USA]). These quality checksrevealed no major differences in point estimates (which is the basis ofthe PAR estimates) and lower confidence limits (which is the basis ofcomparison with the ORs), but the upper confidence limits were higher(results not shown).

Interaction Analyses

We used logistic regression modeling to build a model of the jointcontribution of CFH and LOC387715, CFH and cigarette smoking, andLOC387715 and cigarette smoking. A series of models were fitted in orderto draw inferences about the most likely and most parsimonious model(s).As described by North et al. (2005) models were compared by using theAkaike information criterion (AIC). When the most parsimonious model hadbeen identified we estimated joint ORs of the risk factors. Separateestimates were calculated from each cohort. In order to maximize theAREDS sample size, no subphenotype or subgrade analyses were performed;AREDS cases of grade 3-5 were compared to AREDS controls of grade 1.

In a previous paper (Jakobsdottir (2005)) we found no evidence ofinteracting effects of the CFH and PLEKHA1/LOC387715 loci; the jointaction of the two loci was best described by independent multiplicativeeffects (additive on a log-scale). Rivera et al. (2005) reported thatS69A in LOC387715 acted independently of Y402H in CFH. Schmidt et al.(2006a) also arrived at the same most parsimonious model, and here,again, this model is most parsimonious in both AREDS and CHS data sets(Table 19). Joint ORs for combinations of risk genotypes at Y402H andS69A were computed to further understand the joint action of the twoloci (Table 20). Using all cases regardless of severity, the AREDS datasuggest that individuals heterozygote for the risk allele at one of theloci and homozygote for the non-risk allele at the other are moresusceptible to ARM than individuals with no risk allele at both loci(for the CT-GG joint genotype, OR 2.8, 95% CI 1.6-5.0; for the TT-GTjoint genotype, OR 3.2, 95% CI 1.7-6.0). The ARM risk more then doublesif a person is heterozygote at both loci (for the CT-GT joint genotype,OR 7.2, 95% CI 3.8-13.5) and being homozygote for the risk allele for atleast one of the loci further increases the risk. The joint ORsestimated from the CHS data show a similar pattern, but having only onerisk allele is not sufficient to increase the risk (for the CT-GG jointgenotype, OR 1.3, 95% CI 0.6-2.7; for the TT-GT joint genotype, OR 1.2,95% CI 0.5-2.8).

TABLE 19 Results of fitting two-factor models by logistic regression.AREDS data CHS data AIC AIC Two-Factor Model AIC Difference AICDifference Y402H (Factor 1) and S69A (Factor 2) ADD1 799.3 77.9 652.717.6 ADD2 786.1 64.7 656.0 21.0 ADD-BOTH 723.0 1.7 635.1 0.0 DOM1 801.279.8 654.4 19.3 DOM2 786.9 65.5 656.0 21.0 DOM-BOTH 726.5 5.1 636.3 1.3ADD-INT 721.4 0.0 635.8 0.8 ADD-DOM 724.3 3.0 638.8 3.8 DOM-INT . . . .. . 637.8 2.8 Y402H (Factor 1) and Smoking (ever vs. never) ADD1 787.36.0 677.3 0.0 SMOKE 848.3 67.0 700.6 23.3 ADD1-SMOKE 781.3 0.0 679.1 1.8DOM1 789.3 8.0 679.0 1.7 ADD1-SMOKE-INT 783.2 1.8 678.3 1.0DOM1-SMOKE-INT 786.6 5.3 681.9 4.6 S69A (Factor 2) and Smoking (ever vs.never) ADD2 774.0 6.1 745.6 0.1 SMOKE 842.9 75.0 765.2 19.8 ADD2-SMOKE767.9 0.0 747.3 1.8 DOM2 774.7 6.7 745.5 0.0 ADD2-SMOKE-INT 769.7 1.8749.1 3.7 DOM2-SMOKE-INT 772.4 4.4 748.9 3.4 Detailed model definitionsare given in the “Materials and Methods - Interaction Analyses” section.AIC difference is the difference from the AIC of the best fitting model.Most parsimonious model is in bold. Model with best fit (lowest AIC) hasAIC difference = 0.

TABLE 20 Joint Ors and 95% CIs at Y402H in CFH and S69A in LOC387715Analyzed cohort OR (95% CI) for and size Y402H of sample S69A Maineffects TT CT CC AREDS n_(controls) = 171 n_(cases) = 693 OR_(Y402H)1.00 (Ref) 2.70 (1.83, 3.98) 6.64 (4.04, 10.91) OR_(S69A) Joint effectsGG  1.00 (Ref) 1.00 (Ref) 2.82 (1.59, 5.03) . . . GT  3.03 (2.11, 4.36)3.17 (1.68, 5.96) 7.16 (3.80, 13.49) . . . TT 17.11 (6.17, 47.47) . . .. . . 15.79 (8.74, 28.54) CHS n_(controls) = 871 OR_(Y402H) 1.00 (Ref)1.81 (1.12, 2.93) 4.12 (2.32, 7.33) n_(cases) = 106 OR_(S69A) Jointeffects GG  1.00 (Ref) 1.00 (Ref) 1.31 (0.64, 2.69) . . . GT  1.59(1.03, 2.47) 1.22 (0.53, 2.83) 2.90 (1.47, 5.73) . . . TT  4.86 (2.55,9.26) . . . . . . 4.82 (2.52, 9.23)^(a) NOTE n_(controls) = number ofcontrols fully typed at both loci, n_(cases) = number of cases fullytyped at both loci. OR_(Y402H) = OR for Y402H averaged across S69Agenotypes, OR_(S69A) = OR for S69A averaged across Y402H genotypes.^(a)OR for individuals homozygote at least at one of the loci.

A recent study (Schmidt et al. (2006a)) reported a strong statisticalinteraction between genotypes at S69A and smoking, both on binary (evervs. never smoked) and continuous scale (pack-years of smoking). We failto replicate this finding in both the AREDS and CHS data sets (Table19). Results from the AREDS sample suggests that the joint effects ofY402H and smoking are best described by independent multiplicativeeffects, without significant dominance or interacting effects. On theother hand, the model that best describes the CHS data includes onlyadditive effects of Y402H. Results from the AREDS data suggest that thejoint effects of S69A and smoking are best described by independentmultiplicative effects, without significant dominance or interactingeffects. The CHS data implicate a model with only S69A. When smokingexposure is continuous variable (pack-years of smoking) and the S69Agenotypes are coded in additive fashion, the interaction term is notsignificant (P=0.40) in the CHS data. Pack-years of cigarette smokingwere not available for participants in the AREDS study. To furtherunderstand the combined effect of the genes and cigarette smoking, jointORs of risk genotypes at each gene and smoking were estimated from theAREDS data (Table 21). The results suggest that, while the risk of ARMdue any of the risk genotypes (at Y402H and S69A) is elevated insmokers, both genes have substantially more influence on ARM risk thancigarette smoking. Both the model fitting approach and a simplechi-squared test (P=0.71) show that the main effects of cigarettesmoking are insignificant (on binary scale) in the CHS data.

TABLE 21 Joint Ors and 95% CIs at Y402H in CFH and smoking, and S69A inLOC387715 and smoking AREDS cohort OR (95% CI) for Gene (Variant) andSmoking history Genotypes Main effects Never Ever CFH (Y402H)n_(controls) = 170 OR_(smk) 1.00 (Ref) 1.59 (1.13, 2.23) n_(cases) = 682OR_(Y402H) Joint effects) TT 1.00 (Ref) 1.00 (Ref) 1.65 (0.91, 2.98) CT2.65 (1.79, 3.90) 2.53 (1.43, 4.48) 4.77 (2.66, 8.54) CC 7.27 (4.37,12.09) 8.65 (4.03, 18.55) 10.55 (5.14, 21.66)) LOC387715 (S69A)n_(controls) = 169 OR_(smk) 1.00 (Ref) 1.57 (1.12, 2.20) n_(cases) = 676OR_(S69A) Joint effects) GG 1.00 (Ref) 1.00 (Ref) 1.77 (1.11, 2.83) GT2.98 (2.07, 4.29) 3.19 (1.87, 5, 41) 5.06 (2.99, 8.55) TT 17.02 (6.13,47.26) 21.15 (4.96, 90.22) 25.74 (6.06, 109.34) CHS cohort OR (95% CI)for Gene (Variant) and Smoking history Genotypes Main effects Never EverCFH (Y402H) n_(controls) = 907 OR_(smk) 1.00 (Ref) 0.89 (0.60, 1.32)n_(cases) = 110 OR_(Y402H) Joint effects) TT 1.00 (Ref) 1.00 (Ref) 0.62(0.29, 1.33) CT 1.82 (1.13, 2.92) 1.52 (0.80, 2.91) 1.33 (0.69, 2.58) CC4.22 (2.39, 7.42) 2.52 (1.10, 5.79) 4.16 (1.95, 8.86)) LOC387715 (S69A)n_(controls) = 995 OR_(smk) 1.00 (Ref) 0.89 (0.61, 1.30) n_(cases) = 120OR_(S69A) Joint effects) GG 1.00 (Ref) 1.00 (Ref) 0.96 (0.55, 1.68) GT1.58 (1.05, 2.39) 1.86 (1.04, 3.31) 1.28 (0.70, 2.34) TT 4.75 (2.56,8.80) 3.37 (1.32, 8.63) 6.09 (2.63, 14.14)

APOE Results: Main effects of the APOE gene in ARM were tested using theCHS data. Neither the distribution of APOE-ε4 carriers (P=0.41) norAPOE-ε2 (P=0.42) carriers was significantly different between cases andcontrols, when compared to APOE-ε3/ε3.

Meta-Analyses

Meta-analysis of CFH: We used a meta-analysis approach to pool estimatedORs for Y402H from eleven independent data sets (including the CHS andAREDS cohorts reported here (Table 14). This resulted in the analysis of5,451 cases and 3,540 controls all of European or European Americandescent. The results confirm the increased ARM risk due to the C allelein the non-Hispanic white population (FIG. 12 and Table 22). The pooledestimates have narrower CI than any individual study, andnon-overlapping CI for hetero- and homozygote ORs: OR_(het)=2.43 (95% CI2.17-2.72) and OR_(hom)=6.22 (95% CI 5.38-7.19), when assuminghomogeneity across studies. When the analysis is performed underheterogeneity, the point estimates are essentially the same and the CIsare slightly wider. Leave-one-out sensitivity analysis, under a fixedeffect model show that no study has dramatic influence on the pooledestimates (Table 22).

The study by Rivera et al. (2005) changes the estimates more than anyother study; when the study is excluded, the OR_(dom) and OR_(het) arelowered by approximately 0.2, while the OR_(rec) and OR_(hom) areapproximately 0.2 higher. The Rivera et al. (2005) study is the onlystudy where the genotype distribution, in the control group, deviatesfrom HWE (P=0.03). The allele and genotype distributions, in cases andcontrols, are strikingly similar across studies. However, the genotypedistribution in CHS cases differs from the other studies and thefrequency of the TT risk genotype is lower compared to other cohorts(FIG. 13).

TABLE 22 Results of meta-analysis of Y402H in CFH. ORs (95% CIs)estimated from individual studies and all studies pooled. Results ofleave-one-out sensitivity analysis are shown. DOMINANCE RECESSIVEHETEROZYGOTES HOMOZYGOTES (CT + CC vs. TT) (CC vs. CT + TT) (CT vs. TT)(CC vs. TT) ORs for OR_(dom) (95% CI) OR_(rec) (95% CI) OR_(het) (95%CI) OR_(hom) (95% CI) Individual study Edwards et al. 2.71 (1.86, 3.94)2.89 (1.82, 4.60) 2.14 (1.44, 3.18)  4.54 (2.70, 7.65) Haines et al. . .. . . . 2.45 (1.41, 4.25)  3.33 (1.79, 6.20) Zareparsi et al. 4.36(3.13, 6.08) 5.52 (3.54, 8.59) 3.03 (2.15, 4.28) 11.61 (7.05, 19.14)Hageman et al. (Columbia) 2.97 (2.17, 4.07) 2.61 (1.76, 3.87) 2.48(1.77, 3.47)  4.47 (2.89, 6.93) Hageman et al. (Iowa) 3.64 (2.38, 5.58)4.08 (2.33, 7.16) 2.61 (1.66, 4.10)  7.28 (3.92, 13.51) Jakobsdottir etal. 5.29 (3.35, 8.35) 4.57 (2.48, 8.42) 3.78 (2.32, 6.17) 10.05 (5.16,19.59) Rivera et al. 2.92 (2.39, 3.57) 4.29 (3.42, 5.39) 1.99 (1.61,2.46)  6.72 (5.14, 8.79) Souied et al. 3.95 (2.22, 7.03) 3.75 (1.83,7.71) 2.99 (1.61, 5.57)  6.84 (3.07, 15.21) Sepp et al. 3.85 (2.71,5.47) 3.36 (2.28, 4.95) 2.88 (1.98, 4.20)  6.49 (4.12, 10.23) AREDS (1vs. 345) 3.73 (2.60, 5.34) 3.69 (2.37, 5.75) 2.66 (1.81, 3.92)  6.69(4.08, 10.98) CHS 2.26 (1.45, 3.53) 2.99 (1.85, 4.83) 1.82 (1.13, 2.92) 4.22 (2.39, 7.42) All studies pooled Fixed effects 3.33 (2.99, 3.71)$\frac{P^{a}}{0.11}$ 3.75 (3.29, 4.27) $\frac{P^{a}}{0.32}$ 2.43 (2.17,2.72) $\frac{P^{a}}{0.32}$  6.22 (5.38, 7.19) $\frac{P^{a}}{0.05}$Random effects 3.40 (2.88, 4.00) . . . 3.70 (3.09, 4.42) . . . 2.49(2.14, 2.89) . . .  6.15 (4.86, 7.79) . . . Study excluded (Fixedeffects) Edwards et al. 3.39 (3.03, 3.80) $\frac{\Delta^{b}}{- 0.06}$3.83 (3.35, 4.39) $\frac{\Delta^{b}}{- 0.08}$ 2.46 (2.19, 2.77)$\frac{\Delta^{b}}{- 0.03}$  6.39 (5.49, 7.42)$\frac{\Delta^{b}}{- 0.17}$ Haines et al. . . . . . . . . . . . . 2.43(2.17, 2.73) 0.00  6.45 (5.56, 7.48) −0.23 Zareparsi et al. 3.22 (2.87,3.61) 0.11 3.62 (3.16, 4.14) 0.13 2.37 (2.10, 2.67) 0.06  5.88 (5.05,6.83) 0.35 Hageman et al. (Columbia) 3.38 (3.01, 3.79) −0.05 3.92 (3.42,4.50) −0.17 2.43 (2.15, 2.74) 0.01  6.48 (5.56, 7.55) −0.26 Hageman etal. (Iowa) 3.31 (2.96, 3.70) 0.02 3.73 (3.27, 4.26) 0.02 2.42 (2.16,2.72) 0.01  6.16 (5.31, 7.15) 0.06 Jakobsdottir et al. 3.24 (2.89, 3.62)0.09 3.72 (3.25, 4.24) 0.03 2.37 (2.11, 2.67) 0.06  6.08 (5.24, 7.05)0.15 Rivera et al. 3.51 (3.09, 3.99) −0.18 3.52 (3.00, 4.12) 0.23 2.63(2.30, 3.00) −0.20  6.03 (5.08, 7.16) 0.19 Souied et al. 3.31 (2.96,3.69) 0.02 3.75 (3.29, 4.28) 0.00 2.42 (2.15, 2.71) 0.02  6.20 (5.35,7.18) 0.02 Sepp et al. 3.28 (2.92, 3.67) 0.05 3.80 (3.31, 4.36) −0.052.39 (2.13, 2.69) 0.04  6.19 (5.32, 7.21) 0.03 AREDS (1 vs. 345) 3.29(2.94, 3.68) 0.04 3.76 (3.28, 4.30) −0.01 2.41 (2.14, 2.72) 0.02  6.18(5.31, 7.19) 0.04 CHS 3.41 (3.05, 3.81) −0.08 3.82 (3.34, 4.37) −0.072.48 (2.20, 2.78) −0.04  6.39 (5.50, 7.42) −0.17 ^(a)P-value for test ofhomogeneity of ORs across studies. ^(b)Difference (Δ) of pooled pointestimate when a study is excluded from the pooled estimate of allstudies (under fixed effects model)

Meta-analysis of LOC387715: Meta-analysis of the risk associated withS69A in ARM included five independent data sets (including the CHS andAREDS cohorts reported here (FIG. 14 and Table 15). This resulted in theanalysis of 3,193 cases and 2,405 controls all of European or EuropeanAmerican descent. The studies of LOC387715 are more heterogeneous thenthe studies of CFH; OR_(dom) and OR_(het) differ significantly acrossstudies (P<0.01 and 0.02, respectively). The results support earlierfindings of the association of the T allele with increased ARM risk(Table 23). Carriers of two T alleles are at substantially higher riskthen are carriers of one T allele; when accounting for between-studyvariation, the OR_(het) and OR_(hom) are 2.48 (95% CI 1.67-3.70) and7.33 (95% CI 4.33-12.42), respectively. The genotype distribution issimilar across all control populations and across all ARM populations,except the CHS ARM population (FIG. 15).

TABLE 23 Results of meta-analysis of S69A in LOC387715. ORs (95% CIs)estimated from individual studies and all studies pooled. Results ofleave-one-out sensitivity analysis are shown. DOMINANCE RECESSIVEHETEROZYGOTES HOMOZYGOTES (GT + TT vs. GG) (TT vs. GT + GG) (GT vs. GG)(TT vs. GG) OR for OR_(dom) (95% CI) OR_(rec) (95% CI) OR_(het) (95% CI)OR_(hom) (95% CI) Individual study Jakobsdottir et al. 5.03 (3.20, 7.91) 5.75 (2.46, 13.46) 3.89 (2.40, 6.31) 10.57 (4.43, 25.22) Rivera et al.3.41 (2.84, 4.09)  5.28 (3.76, 7.41) 2.69 (2.22, 3.27)  8.21 (5.79,11.65) Schmidt et al. (1 vs. 345) 2.42 (1.75, 3.35)  3.59 (1.99, 6.47)1.94 (1.37, 2.74)  4.87 (2.65, 8.95) AREDS (1 vs. 345) 3.99 (2.81, 5.67)10.16 (3.70, 27.88) 3.06 (2.13, 4.39) 17.26 (6.22, 47.89) CHS 1.93(1.32, 2.83)  3.91 (2.17, 7.03) 1.58 (1.05, 2.39)  4.75 (2.56, 8.80) Allstudies pooled Fixed effects 3.19 (2.80, 3.63) $\frac{P^{a}}{< 0.01}$ 4.91 (3.85, 6.27) $\frac{P^{a}}{0.41}$ 2.53 (2.20, 2.90)$\frac{P^{a}}{0.02}$  7.32 (5.69, 9.42) $\frac{P^{a}}{0.11}$ Randomeffects 3.15 (2.02, 4.90) . . .  4.91 (3.48, 6.94) . . . 2.48 (1.67,3.70) . . .  7.33 (4.33, 12.42) . . . Study excluded (Fixed effects)Jakobsdottir et al. 3.06 (2.67, 3.51) $\frac{\Delta^{b}}{0.13}$  4.84(3.76, 6.24) $\frac{\Delta^{b}}{0.07}$ 2.43 (2.11, 2.81)$\frac{\Delta^{b}}{0.09}$  7.08 (5.44, 9.21) $\frac{\Delta^{b}}{0.24}$Rivera et al. 2.98 (2.48, 3.58) 0.21  4.54 (3.20, 6.45) 0.37 2.37 (1.95,2.88) 0.16  6.48 (4.51, 9.31) 0.84 Schmidt et al. (1 vs. 345) 3.36(2.92, 3.87) −0.17  5.24 (4.01, 6.85) −0.33 2.66 (2.29, 3.08) −0.13 7.97 (6.04, 10.51) −0.65 AREDS (1 vs. 345) 3.08 (2.68, 3.54) 0.11  4.70(3.65, 6.04) 0.22 2.45 (2.11, 2.84) 0.08  6.93 (5.34, 8.98) 0.40 CHS3.41 (2.97, 3.91) −0.22  5.15 (3.94, 6.73) −0.24 2.68 (2.32, 3.10) −0.15 7.99 (6.06, 10.52) −0.66 ^(a)P-value for test of homogeneity of ORsacross studies. ^(b)Difference (Δ) of pooled point estimate when a studyis excluded from the pooled estimate of all studies (under fixed effectsmodel)

Major discoveries of associations of the CFH and PLEKHA1/LOC387715 geneswith ARM have been published after the findings of Example 1. A numberof reports established a strong association of the Y402H coding changein CFH with ARM and three reports found association with ARM of the S69Acoding change in LOC387715 that was of similar magnitude as theassociation of Y402H. Both of those genes lie within chromosomalregions, CFH on 1q31 and LOC387715 on 10q26, consistently identified byfamily-based linkage studies (Seddon, J. M., et al. (2003); Majewski,J., et al. (2003); Iyengar, S. K., et al. (2004); Weeks, D. E., et al.(2001) Age-related maculopathy: an expanded genome-wide scan withevidence of susceptibility loci within the 1q31 and 17q25 regions. Am JOphthalmol. 132, 682-692; Weeks, D. E., et al. (2004); Klein, M. L., etal. (1998); and Kenealy, S. J., et al. (2004)).

Because the majority of the studies of Y402H and all three studies ofS69A were specially designed to search for (and find) genes involved inARM complex etiology, it is possible that they overestimate the effectsize of the risk alleles at Y402H and S69A. Therefore, two independentcase-control cohorts were analyzed with minimal inclusion and exclusioncriterion based on ARM status, the AREDS and CHS cohorts. The AREDScohort did have health-related inclusion and exclusion criterionincluding criterion based on eye disease status; however, both affectedand non-affected individuals were enrolled (Age-Related Eye DiseaseStudy Research Group (1999) The Age-Related Eye Disease Study (AREDS):design implications. AREDS report no. 1. Control Clin Trials. 20,573-600). The CHS cohort is a population-based cohort that utilizedcommunity-based recruitment of individuals 65 years and older withminimal inclusion and exclusion criteria (Fried et al. (1991)). Retinalassessments were conducted during the eighth year follow-up visit andretinal diseases were not a factor for recruitment. Given the differencein ascertainment of subjects into the two studies, replication ofassociation of a candidate gene in both cohorts greatly strengthens thesupport for its causal involvement in ARM pathogenesis.

We evaluated associations of four genes, CFH (1q31), ELOVL4 (6q14),PLEKAH1 (10q26), and LOC387715 (10q26). Both CFH and LOC387715 areextremely significantly (P≦0.00001) associated with ARM in both AREDSand CHS cohorts. Both genes show an allele-dose effect on the ARM riskand a model of independent multiplicative contribution of the two genesis most parsimonious in both AREDS and CHS cohorts. The A320T codingchange in the PLEKHA1 gene, adjacent to and in linkage disequilibriumwith LOC387715 on 10q26, is significantly associated with ARM in theAREDS cohort (P=0.00004) but not in the CHS cohort (P=0.08). Theseresults based on applying the haplotype method to both the AREDS and CHScohorts, combined with the findings of Rivera et al. (2005), who usedconditional haplotype analysis and detected, for the first time, a weakexpression of LOC387715 in the retina, and Schmidt et al. (2006), whodetected only a weak association signal at PLEKHA1, strongly indicatethat S69A in LOC387715 is a major ARM-predisposing variant on 10q26. Theresults of the haplotype method show that PLEKHA1 may not be sufficientto account for the ARM-predisposition at 10q26; however A320T in PLEKHA1cannot be excluded as a causative haplotype with S69A and other unknownvariants.

The replication of associations of CFH and LOC387715 genes with ARM inAREDS and CHS cohorts, two cohorts with different ascertainment schemes,continues to provide strong support for their involvement in ARM.Variable findings for PLEKHA1 in AREDS and CHS cohorts do however needto be considered in the light of differences between the two cohorts. Inaddition to differences in ascertainment of the case and controlpopulations, the evaluation of retinal changes, documentation of retinalfindings, and prevalence of advanced ARM differed between the twocohorts. In the CHS study, fundus photography was only available for onerandomly selected eye and the photography was performed with non dilatedpupils and these limitations could certainly influence the sensitivityto detect disease pathology, although this is more likely to influencethe detection of early retinal changes. The proportion of advanced ARMin the entire CHS cohort that was evaluated at the 8 year follow upevaluation was approximately 1.3% (Klein, R., Klein, B. E., Marino, E.K., Kuller, L. H., Furberg, C., Burke, G. L. and Hubbard, L. D. (2003)Early age-related maculopathy in the cardiovascular health study.Ophthalmology. 110, 25-33) compared to approximately 17% in the AREDS(Age-Related Eye Disease Study Research Group (2000)) and the variationin the proportion of advanced ARM disease pathology between the twocohorts could lead to variation in findings, especially if a gene ismore likely to influence progression of the disease. Additionally, oneimportant difference between these two cohorts is the timing of theretinal evaluations. AREDS participants had retinal evaluationsconducted at baseline as well as during follow-up evaluations, while CHSparticipants had retinal evaluations done 8 or more years afterenrollment, when they would have been at least 73 years old. It ispossible that survival to the retinal evaluation for the CHSparticipants could bias the population available for this particulartype of study. It also should be noted that in the AREDS cohort,subjects in categories other than the unaffected group were randomizedinto a clinical trial using vitamin and mineral supplements to evaluatethe impact of these on ARM progression. The effect of this is not clear.

As mentioned previously, most studies that have investigated the geneticetiology of ARM were designed to optimize identification of regions ofthe genome housing susceptibility genes for ARM and for ARM candidategene testing. Utilizing these retrospective studies to estimateattributable risk may lead to overestimates. Published attributablerisks range from 43% to 68% (Edwards et al. (2005); Haines et al.(2005); Jakobsdottir (2005); and Schmidt et al. (2006)) for the Y402Hvariant in CFH and from 36% to 57% (Jakobsdottir (2005) and Schmidt etal. (2006)) for the S69A variant in LOC387715. Interestingly, theadjusted population attributable risks (PARs) for the CHS population arelower than those previously published: 38% for the Y402H variant in CFHand 25% for the S69A variant in LOC387715 (Table 13). Because themajority of the CHS cases have moderate ARM the PAR estimates derivedfrom the CHS data are not completely comparable to estimates fromprevious studies where the proportion of patients with advanced ARM wasconsiderably higher. However, they are comparable to estimates derivedfrom using AREDS cases of grade 3. Those estimates are within in thepreviously published range of PARs: 49% for Y402H in CFH and 46% forS69A in LOC387715. These findings may indicate that the risk of ARMattributed to these two susceptibility variants may be lower thanpreviously thought given that the CHS cohort was not ascertained basedon ARM status. A prospective design is needed to more precisely estimatethe relative risks, which are approximated by ORs estimated fromretrospective case-control designs, and corresponding PARs.

We were not able to replicate the association of ELOVL4 with overall ARM(Conley et al. (2005)). The number of individuals with exudative ARMallowed us to perform subphenotype analysis in the AREDS but not the CHScohort. Subphenotype analysis was especially important with regard toELOVL4, where our previous findings indicated a role for ELOVL4 inexudative ARM; this is (weakly) supported in the AREDS cohort. Given thelack of strong association and significant ORs for ELOVL4 in ARMsusceptibility in both cohorts and the lack of association reported byAyyagari et al., it is very unlikely that ELOVL4 plays a substantialrole in ARM susceptibility. The power to detect an OR of 0.6 for overallARM is reasonable, with type I error rate 5%, minor allele frequency0.15, and population prevalence 6% the power is ˜81% in AREDS and ˜69%in CHS. The power to detect the same effect in exudative ARM is only˜53% in AREDS data, under the same conditions. Therefore the possibilitythat ELOVL4 plays a role in overall ARM is unlikely but mild effect inexudative ARM cannot be refuted. These power estimates were performedusing QUANTO (Gauderman, W. J. and Morrison, J. M. (2006) QUANTO 1.1: Acomputer program for power and sample size calculations forgenetic-epidemiology studies).

The AREDS and CHS data support the independent contribution of Y402H inCFH and S69A in LOC387715 to ARM susceptibility. A multiplicative riskmodel for these two variants is the most parsimonious based onevaluation of the AREDS and CHS cohorts; this model was also supportedby our previous paper Jakobsdottir et al. (2005) as well as datapresented by Rivera et al. (2005) and Schmidt et al. (2006a). The ARMrisk appears to increase as the total number of risk alleles at Y402Hand S69A increases (Table 20).

Prior to the discovery of CFH and LOC387715 cigarette smoking was one ofthe more important known ARM-related risk factors. Cigarette smoking isgenerally accepted as a modifiable risk factor for ARM; van Leeuwen etal. provide a review of the epidemiology of ARM and discuss the supportof smoking as ARM risk factor (van Leeuwen, R., Klaver, C. C.,Vingerling, J. R., Hofman, A. and de Jong, P. T. (2003) Epidemiology ofage-related maculopathy: a review. Eur J Epidemiol. 18, 845-854).Schmidt et al. (2006) recently reported statistically significantinteraction between LOC387715 and cigarette smoking in ARM. Their datasuggested that the association of LOC387715 with ARM was primarilydriven by the gene effect in heavy smokers. Our own analyses ofinteraction do not support this finding and the AREDS data suggest thatthe joint action of S69A and smoking is multiplicative.

A role for CFH and LOC387715 in ARM susceptibility is further supportedvia the results of our meta-analysis. The meta-analysis, which includethe CHS and AREDS cohorts reported in this paper, indicates that havingone or two copies of the risk allele at CFH or LOC387715 increases therisk of ARM, and with those who have two copies are at higher risk. Thecombined results from all studies as well as the results from eachindependent study were remarkably tight (FIGS. 12 and 14). One knownlimitation of meta-analysis is the susceptibility to publication bias.Generally, such bias is a result of non-publication of negative findings(Normand, S. L. (1999) Meta-analysis: formulating, evaluating,combining, and reporting. Stat Med. 18, 321-359). In the case of CFH andLOC387715, all published studies have reported strong association withARM in the same direction with the risk allele for CFH being the allelethat codes for histidine and the risk allele for LOC387715 being theallele that codes for serine. Preferential publication of statisticallysignificant associations were expected to show random directionality ifthe significant association is a false-positive result (Lohmueller, K.E., Pearce, C. L., Pike, M., Lander, E. S. and Hirschhorn, J. N. (2003)Meta-analysis of genetic association studies supports a contribution ofcommon variants to susceptibility to common disease. Nat Genet. 33,177-182). It is therefore unlikely that the consistency of theassociation of CFH and LOC387715 with ARM is a result of publicationbias.

While the results of our statistical analyses are in agreement withLOC387715 being the major ARM-related gene on 10q26, they do not provecausality. The possible causal role of CFH in ARM pathogenesis has beenfurther supported by the localization of its protein within drusendeposits of ARM patients and involvement in activation of the complementpathway. Regarding LOC387715, little is currently known about thebiology of the gene and nothing about how its protein may affect ARMsusceptibility. Until recently the expression of LOC387715 appearedlimited to the placenta but recently weak expression was reported in theretina (Rivera et al. (2005)), which opens up the possibility of atissue-specific role of the gene.

In summary, the results presented in this Example continue to support arole of both CFH and LOC387715 in etiology of ARM, given that both genesare highly associated with ARM regardless of how the subjects wereascertained. Evaluation of PLEKHA1 and ELOVL4 in the AREDS and CHScohorts demonstrates that these genes are much less likely to play rolein ARM susceptibility. The CFH and LOC387715 genes appear to actindependently in a multiplicative way in ARM pathogenesis andindividuals homozygote for the risk alleles at either locus are athighest risk.

Having described this invention above, it will be understood to those ofordinary skill in the art that the same can be performed within a wideand equivalent range of conditions, formulations and other parameterswithout affecting the scope of the invention or any embodiment thereof.

1-33. (canceled)
 34. A method of determining risk of developing severeAge-Related Maculopathy in a human subject comprising detecting thepresence of a thymine or guanine at base 270 of SEQ ID NO: 20 (rs10490924) from a sample obtained from the subject, wherein the presenceof thymine for one or both alleles indicates increased risk ofdeveloping severe Age-Related Maculopathy and the presence of guaninefor both alleles indicates decreased risk of developing severeAge-Related Maculopathy.
 35. The method of claim 34 further comprisingexamining one or more allelic variations in one or more of PLEKHA1,PRSS11 and LOC387715.
 36. The method of claim 34 in which the patienthas one or more symptoms of Age-related Maculopathy and the presence ofthymine-for one or both alleles indicates increased risk of developingend-stage Age-Related Maculopathy and the presence of guanine for bothalleles indicates decreased risk of developing end-stage Age-RelatedMaculopathy.
 37. The method of claim 36 in which the patient has one ormore symptoms of Age-related Maculopathy and the presence of thymine-forone or both alleles indicates increased risk of developing one or bothof geographic atrophy and choroidal neovascular membranes and thepresence of guanine for both alleles indicates decreased risk ofdeveloping one or both of geographic atrophy and choroidal neovascularmembranes.
 38. The method of claim 34 in which the presence ofthymine-for one or both alleles indicates increased risk of developingend-stage Age-Related Maculopathy and the presence of guanine for bothalleles indicates decreased risk of developing end-stage Age-RelatedMaculopathy.
 39. The method of claim 38 in which the presence ofthymine-for one or both alleles indicates increased risk of developingone or both of geographic atrophy and choroidal neovascular membranesand the presence of guanine for both alleles indicates decreased risk ofdeveloping one or both of geographic atrophy and choroidal neovascularmembranes.
 40. A method of differentiating the severity of Age-RelatedMaculopathy in a human subject comprising detecting the presence of athymine or guanine at base 270 of SEQ ID NO: 20 (rs 10490924) from asample obtained from the subject.
 41. The method of claim 40 furthercomprising examining one or more allelic variations in one or more ofPLEKHA1, PRSS11 and LOC387715.
 42. The method of claim 40 in which thepatient has one or more symptoms of Age-related Maculopathy and thepresence of thymine-for one or both alleles indicates increased risk ofdeveloping end-stage Age-Related Maculopathy and the presence of guaninefor both alleles indicates decreased risk of developing end-stageAge-Related Maculopathy.
 43. The method of claim 42 in which the patienthas one or more symptoms of Age-related Maculopathy and the presence ofthymine-for one or both alleles indicates increased risk of developingone or both of geographic atrophy and choroidal neovascular membranesand the presence of guanine for both alleles indicates decreased risk ofdeveloping one or both of geographic atrophy and choroidal neovascularmembranes.
 44. The method of claim 40 in which the presence ofthymine-for one or both alleles indicates increased risk of developingend-stage Age-Related Maculopathy and the presence of guanine for bothalleles indicates decreased risk of developing end-stage Age-RelatedMaculopathy.
 45. The method of claim 44 in which the presence ofthymine-for one or both alleles indicates increased risk of developingone or both of geographic atrophy and choroidal neovascular membranesand the presence of guanine for both alleles indicates decreased risk ofdeveloping one or both of geographic atrophy and choroidal neovascularmembranes.
 46. A method of identifying a human subject having anincreased risk of developing Age-Related Maculopathy comprisingscreening for the presence of a guanine or a thymine at base 270 of SEQID NO: 20 (rs10490924) in a nucleic acid of the subject by examinationof allelic variation in the PLEKHA1/LOC387715/PRSS11 locus of Chromosome10q26, wherein the presence of thymine-for one or both alleles indicatesincreased risk of developing Age-Related Maculopathy and the presence ofguanine for both alleles indicates decreased risk of developingAge-Related Maculopathy.
 47. The method of claim 46, wherein thescreening comprises examination of allelic variation in one or more ofPLEKHA1, PRSS11 and LOC387715.
 48. The method of claim 46, wherein theallelic variation corresponds to the single nucleotide polymorphismrs4146894.
 49. The method of claim 46, wherein the allelic variationcorresponds to the single nucleotide polymorphism identified asrs1045216.
 50. The method of claim 46, wherein the allelic variationcorresponds to one or more single nucleotide polymorphism identified asrs1882907.
 51. The method of claim 46, wherein the allelic variationcorresponds to one or more single nucleotide polymorphism identified asrs760336.
 52. The method of claim 46, wherein the allelic variationcorresponds to one or more single nucleotide polymorphism identified asrs763720.
 53. The method of claim 46, wherein the allelic variationcorresponds to one or more single nucleotide polymorphism identified asrs800292.
 54. The method of claim 46, wherein the allelic variationcorresponds to one or more single nucleotide polymorphism identified asrs1483883.
 55. The method of claim 46, wherein the allelic variationcorresponds to one or more single nucleotide polymorphism identified asrs1853886.
 56. The method of claim 46, wherein the allelic variation isan allelic variation in PLEKHA1.
 57. The method of claim 46, wherein thescreening comprises examination of allelic variation in LOC387715. 58.The method of claim 46, wherein the allelic variation is a mutation thatproduces one of a non-functional gene product and altered expression ofa gene product.
 59. The method of claim 46, wherein the allelicvariation is a mutation of one or more of a frameshift mutation, apromoter mutation and a splicing mutation.
 60. The method of claim 46,further comprising identifying in a nucleic acid sample from the subjectthe occurrence of an allelic variant of complement factor H.
 61. Themethod of claim 60, wherein the allelic variant of Complement Factor Hcorresponds to one or more of the single nucleotide polymorphismsidentified as rs800292, rs1061170, and rs1853883.
 62. The method ofclaim 46, wherein the presence of the guanine or the thymine at base 270of SEQ ID NO: 20 (rs10490924) is identified using a nucleic acidamplification assay.
 63. The method of claim 62, wherein the nucleicacid amplification assay comprises one of a PCR, a reverse transcriptasePCR (RT-PCR), an isothermic amplification, a nucleic acid sequence basedamplification (NASBA), a 5’ fluorescence nuclease assay, a molecularbeacon assay and a rolling circle amplification.
 64. The method of claim46, wherein the presence of the guanine or the thymine at base 270 ofSEQ ID NO: 20 (rs10490924) is identified using an array.
 65. The methodof claim 64, wherein the array comprises one or more reagents, sequencesfor identifying in a nucleic acid sample from the subject the occurrenceof an allelic variation corresponding to one or more of the singlenucleotide polymorphisms identified as rs4146894, rs1045216, rs4405249,rs1882907, rs10490923, rs760336, rs763720, and rs1803403.
 66. The methodof claim 46, wherein the Age-Related Maculopathy is Age-related MacularDegeneration.
 67. The method of claim 46 in which the patient has one ormore symptoms of Age-related Maculopathy and the presence of thymine-forone or both alleles indicates increased risk of developing end-stageAge-Related Maculopathy and the presence of guanine for both allelesindicates decreased risk of developing end-stage Age-RelatedMaculopathy.
 68. The method of claim 67 in which the patient has one ormore symptoms of Age-related Maculopathy and the presence of thymine-forone or both alleles indicates increased risk of developing one or bothof geographic atrophy and choroidal neovascular membranes and thepresence of guanine for both alleles indicates decreased risk ofdeveloping one or both of geographic atrophy and choroidal neovascularmembranes.
 69. The method of claim 46 in which the presence ofthymine-for one or both alleles indicates increased risk of developingend-stage Age-Related Maculopathy and the presence of guanine for bothalleles indicates decreased risk of developing end-stage Age-RelatedMaculopathy.
 70. The method of claim 69 in which the presence ofthymine-for one or both alleles indicates increased risk of developingone or both of geographic atrophy and choroidal neovascular membranesand the presence of guanine for both alleles indicates decreased risk ofdeveloping one or both of geographic atrophy and choroidal neovascularmembranes.